# Machine Learning

This page documents all the machine learning algorithms present in the library. In particular, there are algorithms for performing classification, regression, clustering, sequence labeling, anomaly detection, and feature ranking, as well as algorithms for doing more specialized computations.

A good tutorial and introduction to the general concepts used by most of the objects in this part of the library can be found in the svm example program. After reading this example another good one to consult would be the model selection example program. Finally, if you came here looking for a binary classification or regression tool then I would try the krr_trainer first as it is generally the easiest method to use.

The major design goal of this portion of the library is to provide a highly modular and simple architecture for dealing with kernel algorithms. Towards this end, dlib takes a generic programming approach using C++ templates. In particular, each algorithm is parameterized to allow a user to supply either one of the predefined dlib kernels (e.g. RBF operating on column vectors), or a new user defined kernel. Moreover, the implementations of the algorithms are totally separated from the data on which they operate. This makes the dlib implementation generic enough to operate on any kind of data, be it column vectors, images, or some other form of structured data. All that is necessary is an appropriate kernel.

### Paper Describing dlib Machine Learning

    Davis E. King. Dlib-ml: A Machine Learning Toolkit.
       Journal of Machine Learning Research 10, pp. 1755-1758, 2009

    @Article{dlib09,
      author = {Davis E. King},
      title = {Dlib-ml: A Machine Learning Toolkit},
      journal = {Journal of Machine Learning Research},
      year = {2009},
      volume = {10},
      pages = {1755-1758},
    }

## Primary Algorithms

Binary Classification
svm_nu_trainer
svm_c_trainer
svm_c_linear_trainer
svm_c_ekm_trainer
rvm_trainer
svm_pegasos
train_probabilistic_decision_function
Multiclass Classification
one_vs_one_trainer
one_vs_all_trainer
svm_multiclass_linear_trainer
Regression
mlp
krls
krr_trainer
rr_trainer
svr_trainer
rvm_regression_trainer
rbf_network_trainer
Structured Prediction
Problem Instances
structural_svm_sequence_labeling_problem structural_svm_object_detection_problem
structural_svm_problem
structural_object_detection_trainer
structural_sequence_labeling_trainer
structural_svm_problem_threaded
svm_struct_controller_node
svm_struct_processing_node
Unsupervised
kcentroid
linearly_independent_subset_finder
empirical_kernel_map
kkmeans
svm_one_class_trainer
find_clusters_using_kmeans
vector_normalizer
vector_normalizer_pca
Semi-Supervised
linear_manifold_regularizer
discriminant_pca
manifold_regularization_tools
sample_pair find_percent_shortest_edges_randomly find_k_nearest_neighbors find_approximate_k_nearest_neighbors remove_short_edges remove_long_edges remove_percent_longest_edges remove_percent_shortest_edges squared_euclidean_distance use_weights_of_one use_gaussian_weights
Feature Selection
rank_features
sort_basis_vectors

## Other Tools

Validation
cross_validate_trainer
cross_validate_object_detection_trainer
cross_validate_trainer_threaded
cross_validate_multiclass_trainer
cross_validate_regression_trainer
cross_validate_sequence_labeler
test_binary_decision_function
test_multiclass_decision_function
test_regression_function
test_object_detection_function
test_sequence_labeler
Trainer Adapters
reduced
reduced2
batch
probabilistic
verbose_batch
batch_cached
verbose_batch_cached
null_trainer
roc_c1_trainer
roc_c2_trainer
Kernels
radial_basis_kernel
polynomial_kernel
sigmoid_kernel
linear_kernel
offset_kernel
sparse_radial_basis_kernel
sparse_polynomial_kernel
sparse_sigmoid_kernel
sparse_linear_kernel
Function Objects
decision_function
projection_function
distance_function
probabilistic_decision_function
probabilistic_function
normalized_function
one_vs_one_decision_function
multiclass_linear_decision_function
one_vs_all_decision_function
sequence_labeler
Data IO
load_libsvm_formatted_data
save_libsvm_formatted_data
sparse_to_dense
fix_nonzero_indexing
Miscellaneous
simplify_linear_decision_function
fill_lisf
randomize_samples
is_binary_classification_problem
is_sequence_labeling_problem
approximate_distance_function
is_learning_problem
select_all_distinct_labels
pick_initial_centers
find_gamma_with_big_centroid_gap
compute_mean_squared_distance
kernel_matrix
sparse vectors
use_gaussian_weights
dlib/manifold_regularization.h
dlib/manifold_regularization/function_objects_abstract.h
This is a simple function object that takes a single argument which should be an object similar to sample_pair.
linear_manifold_regularizer_ex.cpp.html
use_weights_of_one
dlib/manifold_regularization.h
dlib/manifold_regularization/function_objects_abstract.h
This is a simple function object that takes a single argument and always returns 1
squared_euclidean_distance
dlib/manifold_regularization.h
dlib/manifold_regularization/function_objects_abstract.h
This is a simple function object that computes squared euclidean distance between two matrix objects.
linear_manifold_regularizer_ex.cpp.html
find_k_nearest_neighbors
dlib/manifold_regularization.h
dlib/manifold_regularization/graph_creation_abstract.h
This is a function which finds all the k nearest neighbors of a set of points and outputs the result as a vector of sample_pair objects. It takes O(n^2) where n is the number of data samples. A faster approximate version is provided by find_approximate_k_nearest_neighbors.
remove_short_edges
dlib/manifold_regularization.h
dlib/manifold_regularization/graph_creation_abstract.h
This is a simple function for removing edges with a small distance value from a vector of sample_pairs.
remove_percent_shortest_edges
dlib/manifold_regularization.h
dlib/manifold_regularization/graph_creation_abstract.h
This is a simple function for removing edges with a small distance value from a vector of sample_pairs.
remove_long_edges
dlib/manifold_regularization.h
dlib/manifold_regularization/graph_creation_abstract.h
This is a simple function for removing edges with a large distance value from a vector of sample_pairs.
remove_percent_longest_edges
dlib/manifold_regularization.h
dlib/manifold_regularization/graph_creation_abstract.h
This is a simple function for removing edges with a large distance value from a vector of sample_pairs.
find_approximate_k_nearest_neighbors
dlib/manifold_regularization.h
dlib/manifold_regularization/graph_creation_abstract.h
This function is a simple approximate form of find_k_nearest_neighbors. Instead of checking all possible edges it randomly samples a large number of them and then performs exact k-nearest-neighbors on that randomly selected subset.
find_percent_shortest_edges_randomly
dlib/manifold_regularization.h
dlib/manifold_regularization/graph_creation_abstract.h
This function is a simple approximate form of find_k_nearest_neighbors. Instead of checking all possible edges it randomly samples a large number of them and then returns the best ones.
linear_manifold_regularizer_ex.cpp.html
sample_pair
dlib/manifold_regularization.h
dlib/manifold_regularization/sample_pair_abstract.h
This object is intended to represent an edge in an undirected graph which has data samples at its vertices.
linear_manifold_regularizer_ex.cpp.html
find_clusters_using_kmeans
dlib/svm.h
dlib/svm/kkmeans_abstract.h
This is just a simple linear kmeans clustering implementation.
pick_initial_centers
dlib/svm.h
dlib/svm/kkmeans_abstract.h
This is a function that you can use to seed data clustering algorithms like the kkmeans clustering method. What it does is pick reasonable starting points for clustering by basically trying to find a set of points that are all far away from each other.
kkmeans_ex.cpp.html
kernel_matrix
dlib/svm.h
dlib/svm/kernel_matrix_abstract.h
This is a simple set of functions that makes it easy to turn a kernel object and a set of samples into a kernel matrix. It takes these two things and returns a matrix expression that represents the kernel matrix.
mlp
dlib/mlp.h
dlib/mlp/mlp_kernel_abstract.h

This object represents a multilayer layer perceptron network that is trained using the back propagation algorithm. The training algorithm also incorporates the momentum method. That is, each round of back propagation training also adds a fraction of the previous update. This fraction is controlled by the momentum term set in the constructor.

It is worth noting that a MLP is, in general, very inferior to modern kernel algorithms such as the support vector machine. So if you haven't tried any other techniques with your data you really should.

mlp_ex.cpp.html
mlp_kernel_1
dlib/mlp/mlp_kernel_1.h
This is implemented in the obvious way.
kernel_1a
is a typedef for mlp_kernel_1
krls
dlib/svm.h
dlib/svm/krls_abstract.h
This is an implementation of the kernel recursive least squares algorithm described in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel.

The long and short of this algorithm is that it is an online kernel based regression algorithm. You give it samples (x,y) and it learns the function f(x) == y. For a detailed description of the algorithm read the above paper.

krls_ex.cpp.html
krls_filter_ex.cpp.html
svm_pegasos
dlib/svm.h
dlib/svm/pegasos_abstract.h
This object implements an online algorithm for training a support vector machine for solving binary classification problems.

The implementation of the Pegasos algorithm used by this object is based on the following excellent paper:

> Pegasos: Primal estimated sub-gradient solver for SVM (2007) by Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro In ICML

This SVM training algorithm has two interesting properties. First, the pegasos algorithm itself converges to the solution in an amount of time unrelated to the size of the training set (in addition to being quite fast to begin with). This makes it an appropriate algorithm for learning from very large datasets. Second, this object uses the kcentroid object to maintain a sparse approximation of the learned decision function. This means that the number of support vectors in the resulting decision function is also unrelated to the size of the dataset (in normal SVM training algorithms, the number of support vectors grows approximately linearly with the size of the training set).

svm_pegasos_ex.cpp.html
svm_sparse_ex.cpp.html
kkmeans
dlib/svm.h
dlib/svm/kkmeans_abstract.h
This is an implementation of a kernelized k-means clustering algorithm. It performs k-means clustering by using the kcentroid object.
kkmeans_ex.cpp.html
vector_normalizer
dlib/statistics.h
dlib/statistics/statistics_abstract.h
This object represents something that can learn to normalize a set of column vectors. In particular, normalized column vectors should have zero mean and a variance of one.
svm_ex.cpp.html
discriminant_pca
dlib/statistics.h
dlib/statistics/dpca_abstract.h
This object implements the Discriminant PCA technique described in the paper:

> A New Discriminant Principal Component Analysis Method with Partial Supervision (2009) by Dan Sun and Daoqiang Zhang

This algorithm is basically a straightforward generalization of the classical PCA technique to handle partially labeled data. It is useful if you want to learn a linear dimensionality reduction rule using a bunch of data that is partially labeled.
vector_normalizer_pca
dlib/statistics.h
dlib/statistics/statistics_abstract.h
This object represents something that can learn to normalize a set of column vectors. In particular, normalized column vectors should have zero mean and a variance of one.

This object also uses principal component analysis for the purposes of reducing the number of elements in a vector.
linearly_independent_subset_finder
dlib/svm.h
dlib/svm/linearly_independent_subset_finder_abstract.h

This is an implementation of an online algorithm for recursively finding a set (aka dictionary) of linearly independent vectors in a kernel induced feature space. To use it you decide how large you would like the dictionary to be and then you feed it sample points.

The implementation uses the Approximately Linearly Dependent metric described in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel to decide which points are more linearly independent than others. The metric is simply the squared distance between a test point and the subspace spanned by the set of dictionary vectors.

Each time you present this object with a new sample point it calculates the projection distance and if it is sufficiently large then this new point is included into the dictionary. Note that this object can be configured to have a maximum size. Once the max dictionary size is reached each new point kicks out a previous point. This is done by removing the dictionary vector that has the smallest projection distance onto the others. That is, the "least linearly independent" vector is removed to make room for the new one.

empirical_kernel_map_ex.cpp.html
fill_lisf
dlib/svm.h
dlib/svm/linearly_independent_subset_finder_abstract.h
This is a simple function for filling a linearly_independent_subset_finder with data points by using random sampling.
empirical_kernel_map_ex.cpp.html
sort_basis_vectors
dlib/svm.h
dlib/svm/sort_basis_vectors_abstract.h
A kernel based learning method ultimately needs to select a set of basis functions represented by a particular choice of kernel and a set of basis vectors. sort_basis_vectors() is a function which attempts to perform supervised basis set selection. In particular, you give it a candidate set of basis vectors and it sorts them according to how useful they are for solving a particular decision problem.
linear_manifold_regularizer
dlib/manifold_regularization.h
dlib/manifold_regularization/linear_manifold_regularizer_abstract.h

Many learning algorithms attempt to minimize a function that, at a high level, looks like this:

       f(w) == complexity + training_set_error

The idea is to find the set of parameters, w, that gives low error on your training data but also is not "complex" according to some particular measure of complexity. This strategy of penalizing complexity is usually called regularization.

In the above setting, all the training data consists of labeled samples. However, it would be nice to be able to benefit from unlabeled data. The idea of manifold regularization is to extract useful information from unlabeled data by first defining which data samples are "close" to each other (perhaps by using their 3 nearest neighbors) and then adding a term to the above function that penalizes any decision rule which produces different outputs on data samples which we have designated as being close.

It turns out that it is possible to transform these manifold regularized learning problems into the normal form shown above by applying a certain kind of preprocessing to all our data samples. Once this is done we can use a normal learning algorithm, such as the svm_c_linear_trainer, on just the labeled data samples and obtain the same output as the manifold regularized learner would have produced.

The linear_manifold_regularizer is a tool for creating this preprocessing transformation. In particular, the transformation is linear. That is, it is just a matrix you multiply with all your samples. For a more detailed discussion of this topic you should consult the following paper. In particular, see section 4.2. This object computes the inverse T matrix described in that section.

> Linear Manifold Regularization for Large Scale Semi-supervised Learning by Vikas Sindhwani, Partha Niyogi, and Mikhail Belkin

linear_manifold_regularizer_ex.cpp.html
empirical_kernel_map
dlib/svm.h
dlib/svm/empirical_kernel_map_abstract.h

This object represents a map from objects of sample_type (the kind of object a kernel function operates on) to finite dimensional column vectors which represent points in the kernel feature space defined by whatever kernel is used with this object.

To use the empirical_kernel_map you supply it with a particular kernel and a set of basis samples. After that you can present it with new samples and it will project them into the part of kernel feature space spanned by your basis samples.

This means the empirical_kernel_map is a tool you can use to very easily kernelize any algorithm that operates on column vectors. All you have to do is select a set of basis samples and then use the empirical_kernel_map to project all your data points into the part of kernel feature space spanned by those basis samples. Then just run your normal algorithm on the output vectors and it will be effectively kernelized.

Regarding methods to select a set of basis samples, if you are working with only a few thousand samples then you can just use all of them as basis samples. Alternatively, the linearly_independent_subset_finder often works well for selecting a basis set. I also find that picking a random subset typically works well.

empirical_kernel_map_ex.cpp.html
linear_manifold_regularizer_ex.cpp.html
kcentroid
dlib/svm.h
dlib/svm/kcentroid_abstract.h

This object represents a weighted sum of sample points in a kernel induced feature space. It can be used to kernelize any algorithm that requires only the ability to perform vector addition, subtraction, scalar multiplication, and inner products.

An example use of this object is as an online algorithm for recursively estimating the centroid of a sequence of training points. This object then allows you to compute the distance between the centroid and any test points. So you can use this object to predict how similar a test point is to the data this object has been trained on (larger distances from the centroid indicate dissimilarity/anomalous points).

The object internally keeps a set of "dictionary vectors" that are used to represent the centroid. It manages these vectors using the sparsification technique described in the paper The Kernel Recursive Least Squares Algorithm by Yaakov Engel. This technique allows us to keep the number of dictionary vectors down to a minimum. In fact, the object has a user selectable tolerance parameter that controls the trade off between accuracy and number of stored dictionary vectors.

kcentroid_ex.cpp.html
train_probabilistic_decision_function
dlib/svm.h
dlib/svm/svm_abstract.h

Trains a probabilistic_function using some sort of binary classification trainer object such as the svm_nu_trainer or krr_trainer.

The probability model is created by using the technique described in the following papers:

> Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods by John C. Platt. March 26, 1999

> A Note on Platt's Probabilistic Outputs for Support Vector Machines by Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng

svm_ex.cpp.html
probabilistic
dlib/svm.h
dlib/svm/svm_abstract.h
This is a trainer adapter which simply runs the trainer it is given though the train_probabilistic_decision_function function.
rbf_network_trainer
dlib/svm.h
dlib/svm/rbf_network_abstract.h
Trains a radial basis function network and outputs a decision_function. This object can be used for either regression or binary classification problems. It's worth pointing out that this object is essentially an unregularized version of kernel ridge regression. This means you should really prefer to use kernel ridge regression instead.
rvm_regression_trainer
dlib/svm.h
dlib/svm/rvm_abstract.h

Trains a relevance vector machine for solving regression problems. Outputs a decision_function that represents the learned regression function.

The implementation of the RVM training algorithm used by this library is based on the following paper:

> Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6.

rvm_regression_ex.cpp.html
rvm_trainer
dlib/svm.h
dlib/svm/rvm_abstract.h

Trains a relevance vector machine for solving binary classification problems. Outputs a decision_function that represents the learned classifier.

The implementation of the RVM training algorithm used by this library is based on the following paper:

> Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6.

rvm_ex.cpp.html
krr_trainer
dlib/svm.h
dlib/svm/krr_trainer_abstract.h

Performs kernel ridge regression and outputs a decision_function that represents the learned function.

The implementation is done using the empirical_kernel_map and linearly_independent_subset_finder to kernelize the rr_trainer object. Thus it allows you to run the algorithm on large datasets and obtain sparse outputs. It is also capable of automatically estimating its regularization parameter using leave-one-out cross-validation.
krr_regression_ex.cpp.html
krr_classification_ex.cpp.html
sparse vectors
dlib/svm.h
dlib/svm/sparse_vector_abstract.h
In dlib, sparse vectors are represented using the container objects in the C++ STL. This file defines various useful functions for operating on these sparse vectors.
rr_trainer
dlib/svm.h
dlib/svm/rr_trainer_abstract.h

Performs linear ridge regression and outputs a decision_function that represents the learned function. In particular, this object can only be used with the linear_kernel. It is optimized for the linear case where the number of features in each sample vector is small (i.e. on the order of 1000 or less since the algorithm is cubic in the number of features.). If you want to use a nonlinear kernel then you should use the krr_trainer.

This object is capable of automatically estimating its regularization parameter using leave-one-out cross-validation.
svr_trainer
dlib/svm.h
dlib/svm/svr_trainer_abstract.h

This object implements a trainer for performing epsilon-insensitive support vector regression. It is implemented using the SMO algorithm.

The implementation of the eps-SVR training algorithm used by this object is based on the following paper:

- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

svr_ex.cpp.html
svm_nu_trainer
dlib/svm.h
dlib/svm/svm_nu_trainer_abstract.h

Trains a nu support vector machine for solving binary classification problems and outputs a decision_function. It is implemented using the SMO algorithm.

The implementation of the nu-svm training algorithm used by this library is based on the following excellent papers:

- Chang and Lin, Training {nu}-Support Vector Classifiers: Theory and Algorithms
- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

svm_ex.cpp.html
model_selection_ex.cpp.html
svm_one_class_trainer
dlib/svm.h
dlib/svm/svm_one_class_trainer_abstract.h

Trains a one-class support vector classifier and outputs a decision_function. It is implemented using the SMO algorithm.

The implementation of the one-class training algorithm used by this library is based on the following paper:

- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

svm_c_trainer
dlib/svm.h
dlib/svm/svm_c_trainer_abstract.h

Trains a C support vector machine for solving binary classification problems and outputs a decision_function. It is implemented using the SMO algorithm.

The implementation of the C-SVM training algorithm used by this library is based on the following paper:

- Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

svm_c_linear_trainer
dlib/svm.h
dlib/svm/svm_c_linear_trainer_abstract.h
This object represents a tool for training the C formulation of a support vector machine to solve binary classification problems. It is optimized for the case where linear kernels are used and is implemented using the oca optimizer and uses the exact line search described in the following paper:

> Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization by Vojtech Franc, Soren Sonnenburg; Journal of Machine Learning Research, 10(Oct):2157--2192, 2009.

svm_sparse_ex.cpp.html
svm_c_ekm_trainer
dlib/svm.h
dlib/svm/svm_c_ekm_trainer_abstract.h
This object represents a tool for training the C formulation of a support vector machine for solving binary classification problems. It is implemented using the empirical_kernel_map to kernelize the svm_c_linear_trainer. This makes it a very fast algorithm capable of learning from very large datasets.

normalized_function
dlib/svm.h
dlib/svm/function_abstract.h
This object represents a container for another function object and an instance of the vector_normalizer object.

It automatically normalizes all inputs before passing them off to the contained function object.
svm_ex.cpp.html
probabilistic_decision_function
dlib/svm.h
dlib/svm/function_abstract.h
This object represents a binary decision function for use with kernel-based learning-machines. It returns an estimate of the probability that a given sample is in the +1 class.
svm_ex.cpp.html
probabilistic_function
dlib/svm.h
dlib/svm/function_abstract.h
This object represents a binary decision function for use with any kind of binary classifier. It returns an estimate of the probability that a given sample is in the +1 class.
distance_function
dlib/svm.h
dlib/svm/function_abstract.h
This object represents a point in kernel induced feature space. You may use this object to find the distance from the point it represents to points in input space as well as other points represented by distance_functions.
decision_function
dlib/svm.h
dlib/svm/function_abstract.h
This object represents a classification or regression function that was learned by a kernel based learning algorithm. Therefore, it is a function object that takes a sample object and returns a scalar value.
svm_ex.cpp.html
one_vs_one_decision_function
dlib/svm.h
dlib/svm/one_vs_one_decision_function_abstract.h
This object represents a multiclass classifier built out of a set of binary classifiers. Each binary classifier is used to vote for the correct multiclass label using a one vs. one strategy. Therefore, if you have N classes then there will be N*(N-1)/2 binary classifiers inside this object.
multiclass_classification_ex.cpp.html
custom_trainer_ex.cpp.html
one_vs_one_trainer
dlib/svm.h
dlib/svm/one_vs_one_trainer_abstract.h
This object is a tool for turning a bunch of binary classifiers into a multiclass classifier. It does this by training the binary classifiers in a one vs. one fashion. That is, if you have N possible classes then it trains N*(N-1)/2 binary classifiers which are then used to vote on the identity of a test sample.
multiclass_classification_ex.cpp.html
custom_trainer_ex.cpp.html
one_vs_all_decision_function
dlib/svm.h
dlib/svm/one_vs_all_decision_function_abstract.h
This object represents a multiclass classifier built out of a set of binary classifiers. Each binary classifier is used to vote for the correct multiclass label using a one vs. all strategy. Therefore, if you have N classes then there will be N binary classifiers inside this object.
sequence_labeler
dlib/svm.h
dlib/svm/sequence_labeler_abstract.h
This object is a tool for doing sequence labeling. In particular, it is capable of representing sequence labeling models such as those produced by Hidden Markov SVMs or Conditional Random fields. See the following papers for an introduction to these techniques:

> Hidden Markov Support Vector Machines by Y. Altun, I. Tsochantaridis, T. Hofmann
> Shallow Parsing with Conditional Random Fields by Fei Sha and Fernando Pereira

sequence_labeler_ex.cpp.html
multiclass_linear_decision_function
dlib/svm.h
dlib/svm/function_abstract.h
This object represents a multiclass classifier built out of a set of binary classifiers. Each binary classifier is used to vote for the correct multiclass label using a one vs. all strategy. Therefore, if you have N classes then there will be N binary classifiers inside this object. Additionally, this object is linear in the sense that each of these binary classifiers is a simple linear plane.
one_vs_all_trainer
dlib/svm.h
dlib/svm/one_vs_all_trainer_abstract.h
This object is a tool for turning a bunch of binary classifiers into a multiclass classifier. It does this by training the binary classifiers in a one vs. all fashion. That is, if you have N possible classes then it trains N binary classifiers which are then used to vote on the identity of a test sample.
svm_multiclass_linear_trainer
dlib/svm.h
dlib/svm/svm_multiclass_linear_trainer_abstract.h
This object represents a tool for training a multiclass support vector machine. It is optimized for the case where linear kernels are used and implemented using the structural_svm_problem object.
projection_function
dlib/svm.h
dlib/svm/function_abstract.h
This object represents a function that takes a data sample and projects it into kernel feature space. The result is a real valued column vector that represents a point in a kernel feature space. Instances of this object are created using the empirical_kernel_map.
linear_manifold_regularizer_ex.cpp.html
offset_kernel
dlib/svm.h
dlib/svm/kernel_abstract.h
This object represents a kernel with a fixed value offset added to it.
linear_kernel
dlib/svm.h
dlib/svm/kernel_abstract.h
This object represents a linear function kernel for use with kernel learning machines.
sigmoid_kernel
dlib/svm.h
dlib/svm/kernel_abstract.h
This object represents a sigmoid kernel for use with kernel learning machines.
polynomial_kernel
dlib/svm.h
dlib/svm/kernel_abstract.h
This object represents a polynomial kernel for use with kernel learning machines.
radial_basis_kernel
dlib/svm.h
dlib/svm/kernel_abstract.h
This object represents a radial basis function kernel for use with kernel learning machines.
svm_ex.cpp.html
sparse_linear_kernel
dlib/svm.h
dlib/svm/sparse_kernel_abstract.h
This object represents a linear function kernel for use with kernel learning machines that operate on sparse vectors.
svm_sparse_ex.cpp.html
sparse_sigmoid_kernel
dlib/svm.h
dlib/svm/sparse_kernel_abstract.h
This object represents a sigmoid kernel for use with kernel learning machines that operate on sparse vectors.
sparse_polynomial_kernel
dlib/svm.h
dlib/svm/sparse_kernel_abstract.h
This object represents a polynomial kernel for use with kernel learning machines that operate sparse vectors.
sparse_radial_basis_kernel
dlib/svm.h
dlib/svm/sparse_kernel_abstract.h
This object represents a radial basis function kernel for use with kernel learning machines that operate sparse vectors.
is_binary_classification_problem
dlib/svm.h
dlib/svm/svm_abstract.h
This function simply takes two vectors, the first containing feature vectors and the second containing labels, and reports back if the two could possibly contain data for a well formed classification problem.
is_sequence_labeling_problem
dlib/svm.h
dlib/svm/svm_abstract.h
This function takes a set of training data for a sequence labeling problem and reports back if it could possibly be a well formed sequence labeling problem.
is_learning_problem
dlib/svm.h
dlib/svm/svm_abstract.h
This function simply takes two vectors, the first containing feature vectors and the second containing labels, and reports back if the two could possibly contain data for a well formed learning problem. In this case it just means that the two vectors have the same length and aren't empty.
select_all_distinct_labels
dlib/svm.h
dlib/svm/multiclass_tools_abstract.h
This is a function which determines all distinct values present in a std::vector and returns the result.
simplify_linear_decision_function
dlib/svm.h
dlib/svm/simplify_linear_decision_function_abstract.h
This is a set of functions that takes various forms of linear decision functions and collapses them down so that they only compute a single dot product when invoked.
randomize_samples
dlib/svm.h
dlib/svm/svm_abstract.h
Randomizes the order of samples in a column vector containing sample data.
svm_ex.cpp.html
rank_features
dlib/svm.h
dlib/svm/feature_ranking_abstract.h
Finds a ranking of the top N (a user supplied parameter) features in a set of data from a two class classification problem. It does this by computing the distance between the centroids of both classes in kernel defined feature space. Good features are then ones that result in the biggest separation between the two centroids.
rank_features_ex.cpp.html
load_libsvm_formatted_data
dlib/data_io.h
dlib/data_io/libsvm_io_abstract.h
This is a function that loads the data from a file that uses the LIBSVM format. It loads the data into a std::vector of sparse vectors. If you want to load data into dense vectors (i.e. dlib::matrix objects) then you can use the sparse_to_dense function to perform the conversion. Also, some LIBSVM formatted files number their features beginning with 1 rather than 0. If this bothers you, then you can fix it by using the fix_nonzero_indexing function on the data after it is loaded.
save_libsvm_formatted_data
dlib/data_io.h
dlib/data_io/libsvm_io_abstract.h
This is actually a pair of overloaded functions. Between the two of them they let you save sparse or dense data vectors to file using the LIBSVM format.
sparse_to_dense
dlib/data_io.h
dlib/data_io/libsvm_io_abstract.h
This is a simple function that takes a std::vector of sparse vectors and returns to you the equivalent std::vector of dense vectors.
fix_nonzero_indexing
dlib/data_io.h
dlib/data_io/libsvm_io_abstract.h
This is a simple function that takes a std::vector of sparse vectors and makes sure they are zero-indexed (e.g. makes sure the first index value is zero).
find_gamma_with_big_centroid_gap
dlib/svm.h
dlib/svm/feature_ranking_abstract.h
This is a function that tries to pick a reasonable default value for the gamma parameter of the radial_basis_kernel. It picks the parameter that gives the largest separation between the centroids, in kernel feature space, of two classes of data.
rank_features_ex.cpp.html
compute_mean_squared_distance
dlib/svm.h
dlib/svm/feature_ranking_abstract.h
This is a function that simply finds the average squared distance between all pairs of a set of data samples. It is often convenient to use the reciprocal of this value as the estimate of the gamma parameter of the radial_basis_kernel.
batch
dlib/svm.h
dlib/svm/pegasos_abstract.h
This is a convenience function for creating batch_trainer objects.
svm_pegasos_ex.cpp.html
verbose_batch
dlib/svm.h
dlib/svm/pegasos_abstract.h
This is a convenience function for creating batch_trainer objects. This function generates a batch_trainer that will print status messages to standard output so that you can observe the progress of a training algorithm.
svm_pegasos_ex.cpp.html
batch_cached
dlib/svm.h
dlib/svm/pegasos_abstract.h
This is a convenience function for creating batch_trainer objects that are setup to use a kernel matrix cache.
verbose_batch_cached
dlib/svm.h
dlib/svm/pegasos_abstract.h
This is a convenience function for creating batch_trainer objects. This function generates a batch_trainer that will print status messages to standard output so that you can observe the progress of a training algorithm. It will also be configured to use a kernel matrix cache.
batch_trainer
dlib/svm.h
dlib/svm/pegasos_abstract.h
This is a batch trainer object that is meant to wrap online trainer objects that create decision_functions. It turns an online learning algorithm such as svm_pegasos into a batch learning object. This allows you to use objects like svm_pegasos with functions (e.g. cross_validate_trainer) that expect batch mode training objects.
null_trainer_type
dlib/svm.h
dlib/svm/null_trainer_abstract.h
This object is a simple tool for turning a decision_function (or any object with an interface compatible with decision_function) into a trainer object that always returns the original decision function when you try to train with it.

dlib contains a few "training post processing" algorithms (e.g. reduced and reduced2). These tools take in a trainer object, tell it to perform training, and then they take the output decision function and do some kind of post processing to it. The null_trainer_type object is useful because you can use it to run an already learned decision function through the training post processing algorithms by turning a decision function into a null_trainer_type and then giving it to a post processor.

null_trainer
dlib/svm.h
dlib/svm/null_trainer_abstract.h
This is a convenience function for creating null_trainer_type objects.
roc_c1_trainer
dlib/svm.h
dlib/svm/roc_trainer_abstract.h
This is a convenience function for creating roc_trainer_type objects that are setup to pick a point on the ROC curve with respect to the +1 class.
roc_c2_trainer
dlib/svm.h
dlib/svm/roc_trainer_abstract.h
This is a convenience function for creating roc_trainer_type objects that are setup to pick a point on the ROC curve with respect to the -1 class.
roc_trainer_type
dlib/svm.h
dlib/svm/roc_trainer_abstract.h
This object is a simple trainer post processor that allows you to easily adjust the bias term in a trained decision_function object. That is, this object lets you pick a point on the ROC curve and it will adjust the bias term appropriately.

So for example, suppose you wanted to set the bias term so that the accuracy of your decision function on +1 labeled samples was 99%. To do this you would use an instance of this object declared as follows: roc_trainer_type<trainer_type>(your_trainer, 0.99, +1);

reduced_decision_function_trainer
dlib/svm.h
dlib/svm/reduced_abstract.h
This is a batch trainer object that is meant to wrap other batch trainer objects that create decision_function objects. It performs post processing on the output decision_function objects with the intent of representing the decision_function with fewer basis vectors.
reduced
dlib/svm.h
dlib/svm/reduced_abstract.h
This is a convenience function for creating reduced_decision_function_trainer objects.
reduced2
dlib/svm.h
dlib/svm/reduced_abstract.h
This is a convenience function for creating reduced_decision_function_trainer2 objects.
svm_ex.cpp.html
reduced_decision_function_trainer2
dlib/svm.h
dlib/svm/reduced_abstract.h

This is a batch trainer object that is meant to wrap other batch trainer objects that create decision_function objects. It performs post processing on the output decision_function objects with the intent of representing the decision_function with fewer basis vectors.

It begins by performing the same post processing as the reduced_decision_function_trainer object but it also performs a global gradient based optimization to further improve the results. The gradient based optimization is implemented using the approximate_distance_function routine.

svm_ex.cpp.html
approximate_distance_function
dlib/svm.h
dlib/svm/reduced_abstract.h
This function attempts to find a distance_function object which is close to a target distance_function. That is, it searches for an X such that target(X) is minimized. Critically, X may be set to use fewer basis vectors than the target.

The optimization begins with an initial guess supplied by the user and searches for an X which locally minimizes target(X). Since this problem can have many local minima the quality of the starting point can significantly influence the results.

test_binary_decision_function
dlib/svm.h
dlib/svm/svm_abstract.h
Tests a decision_function that represents a binary decision function and returns the test accuracy.

test_multiclass_decision_function
dlib/svm.h
dlib/svm/cross_validate_multiclass_trainer_abstract.h
Tests a multiclass decision function (e.g. one_vs_one_decision_function) and returns a confusion matrix describing the results.
multiclass_classification_ex.cpp.html
custom_trainer_ex.cpp.html
cross_validate_trainer_threaded
dlib/svm_threaded.h
dlib/svm/svm_threaded_abstract.h
Performs k-fold cross validation on a user supplied binary classification trainer object such as the svm_nu_trainer or rbf_network_trainer. This function does the same thing as cross_validate_trainer except this function also allows you to specify how many threads of execution to use. So you can use this function to take advantage of a multi-core system to perform cross validation faster.
cross_validate_trainer
dlib/svm.h
dlib/svm/svm_abstract.h
Performs k-fold cross validation on a user supplied binary classification trainer object such as the svm_nu_trainer or rbf_network_trainer.
svm_ex.cpp.html
model_selection_ex.cpp.html
cross_validate_multiclass_trainer
dlib/svm.h
dlib/svm/cross_validate_multiclass_trainer_abstract.h
Performs k-fold cross validation on a user supplied multiclass classification trainer object such as the one_vs_one_trainer. The result is described by a confusion matrix.
multiclass_classification_ex.cpp.html
custom_trainer_ex.cpp.html
cross_validate_regression_trainer
dlib/svm.h
dlib/svm/cross_validate_regression_trainer_abstract.h
Performs k-fold cross validation on a user supplied regression trainer object such as the svr_trainer and returns the mean squared error and R-squared value.
svr_ex.cpp.html
cross_validate_sequence_labeler
dlib/svm.h
dlib/svm/cross_validate_sequence_labeler_abstract.h
Performs k-fold cross validation on a user supplied sequence labeling trainer object such as the structural_sequence_labeling_trainer and returns a confusion matrix describing the results.
sequence_labeler_ex.cpp.html
test_sequence_labeler
dlib/svm.h
dlib/svm/cross_validate_sequence_labeler_abstract.h
Tests a sequence_labeler on a set of data and returns a confusion matrix describing the results.
sequence_labeler_ex.cpp.html
cross_validate_object_detection_trainer
dlib/svm.h
dlib/svm/cross_validate_object_detection_trainer_abstract.h
Performs k-fold cross validation on a user supplied object detection trainer such as the structural_object_detection_trainer and returns the precision and recall.
object_detector_ex.cpp.html
object_detector_advanced_ex.cpp.html
test_object_detection_function
dlib/svm.h
dlib/svm/cross_validate_object_detection_trainer_abstract.h
Tests an object detector such as the object_detector and returns the precision and recall.
object_detector_ex.cpp.html
object_detector_advanced_ex.cpp.html
test_regression_function
dlib/svm.h
dlib/svm/cross_validate_regression_trainer_abstract.h
Tests a regression function (e.g. decision_function) and returns the mean squared error and R-squared value.
structural_svm_problem
dlib/svm.h
dlib/svm/structural_svm_problem_abstract.h
This object, when used with the oca optimizer, is a tool for solving the optimization problem associated with a structural support vector machine. A structural SVM is a supervised machine learning method for learning to predict complex outputs. This is contrasted with a binary classifier which makes only simple yes/no predictions. A structural SVM, on the other hand, can learn to predict outputs as complex as entire parse trees. To do this, it learns a function F(x,y) which measures how well a particular data sample x matches a label y. When used for prediction, the best label for a new x is given by the y which maximizes F(x,y).

If you want to see example code that uses this object then take a look at the implementation of the svm_multiclass_linear_trainer object.

For an introduction to structured support vector machines you should consult the following paper:

> Predicting Structured Objects with Support Vector Machines by By Thorsten Joachims, Thomas Hofmann, Yisong Yue, and Chun-nam Yu

For a more detailed discussion of the particular algorithm implemented by this object see the following paper:

> T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of Structural SVMs, Machine Learning, 77(1):27-59, 2009.

Note that this object is essentially a tool for solving the 1-Slack structural SVM with margin-rescaling. Specifically, see Algorithm 3 in the above referenced paper.
structural_svm_problem_threaded
dlib/svm_threaded.h
dlib/svm/structural_svm_problem_threaded_abstract.h
This is just a version of the structural_svm_problem which is capable of using multiple cores/threads at a time. You should use it if you have a multi-core CPU and the separation oracle takes a long time to compute. Or even better, if you have multiple computers then you can use the svm_struct_controller_node to distribute the work across many computers.
structural_svm_object_detection_problem
dlib/svm_threaded.h
dlib/svm/structural_svm_object_detection_problem_abstract.h
This object is a tool for learning the parameter vector needed to use a scan_image_pyramid object.

It learns the parameter vector by formulating the problem as a structural SVM problem. The general approach is similar to the method discussed in Learning to Localize Objects with Structured Output Regression by Matthew B. Blaschko and Christoph H. Lampert. However, the method has been extended to datasets with multiple, potentially overlapping, objects per image and the measure of loss is different from what is described in the paper.

structural_svm_sequence_labeling_problem
dlib/svm_threaded.h
dlib/svm/structural_svm_sequence_labeling_problem_abstract.h
This object is a tool for learning the weight vector needed to use a sequence_labeler object.

It learns the parameter vector by formulating the problem as a structural SVM problem. The general approach is discussed in the paper:

> Hidden Markov Support Vector Machines by Y. Altun, I. Tsochantaridis, T. Hofmann

While the particular optimization strategy used is the method from:

> T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of Structural SVMs, Machine Learning, 77(1):27-59, 2009.

structural_object_detection_trainer
dlib/svm_threaded.h
dlib/svm/structural_object_detection_trainer_abstract.h
This object is a tool for learning to detect objects in images based on a set of labeled images. The training procedure produces an object_detector which can be used to predict the locations of objects in new images.

Note that this is just a convenience wrapper around the structural_svm_object_detection_problem to make it look similar to all the other trainers in dlib.

object_detector_ex.cpp.html
object_detector_advanced_ex.cpp.html
structural_sequence_labeling_trainer
dlib/svm_threaded.h
dlib/svm/structural_sequence_labeling_trainer_abstract.h
This object is a tool for learning to do sequence labeling based on a set of training data. The training procedure produces a sequence_labeler object which can be use to predict the labels of new data sequences.

Note that this is just a convenience wrapper around the structural_svm_sequence_labeling_problem to make it look similar to all the other trainers in dlib.

sequence_labeler_ex.cpp.html
svm_struct_controller_node
dlib/svm_threaded.h
dlib/svm/structural_svm_distributed_abstract.h
This object is a tool for distributing the work involved in solving a structural_svm_problem across many computers.
svm_struct_processing_node
dlib/svm_threaded.h
dlib/svm/structural_svm_distributed_abstract.h
This object is a tool for distributing the work involved in solving a structural_svm_problem across many computers.