mirror of https://github.com/davisking/dlib.git
updated docs
This commit is contained in:
Davis King
parent
d4f5e24587
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<p>
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Dlib is a general purpose cross-platform C++ library designed using contract programming
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and modern C++ techniques.
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Dlib is a modern C++ toolkit containing machine learning algorithms and tools
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for creating complex software in C++ to solve real world problems.
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It is open source software and licensed
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under the <a href="license.html">Boost Software License</a>.
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The <a href="intro.html">introduction</a> contains everything you need to know to get
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started using the library. However, if you have any questions, comments, or complaints feel free to
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<a href='mailto:davis@dlib.net'>email me</a><web> or post in the
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sourceforge <a href='http://sourceforge.net/p/dclib/discussion'>Forums</a></web>.
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started using the library. However, if after consulting the documentation, you have any questions, comments,
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or complaints feel free to post in the
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<a href='http://sourceforge.net/p/dclib/discussion'>forums</a>.
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</p>
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@ -63,6 +63,96 @@
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abstraction layers or is pure ISO standard C++. </li>
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</ul>
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</li>
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<li><b>Machine Learning Algorithms</b>
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<ul>
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<li>Conventional SMO based Support Vector Machines for <a href="ml.html#svm_nu_trainer">classification</a>
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and <a href="ml.html#svr_trainer">regression</a> </li>
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<li>Reduced-rank methods for large-scale <a href="ml.html#svm_c_ekm_trainer">classification</a>
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and <a href="ml.html#krr_trainer">regression</a></li>
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<li>Relevance vector machines for <a href="ml.html#rvm_trainer">classification</a>
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and <a href="ml.html#rvm_regression_trainer">regression</a> </li>
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<li>General purpose <a href="ml.html#one_vs_one_trainer">multiclass classification</a> tools</li>
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<li>A <a href="ml.html#svm_multiclass_linear_trainer">Multiclass SVM</a></li>
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<li>A tool for solving the optimization problem associated with
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<a href="ml.html#structural_svm_problem">structural support vector machines</a>. </li>
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<li>Structural SVM tools for <a href="ml.html#structural_sequence_labeling_trainer">sequence labeling</a> </li>
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<li>Structural SVM tools for solving <a href="ml.html#structural_assignment_trainer">assignment problems</a> </li>
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<li>Structural SVM tools for <a href="ml.html#structural_object_detection_trainer">object detection</a> in images </li>
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<li>Structural SVM tools for <a href="ml.html#structural_graph_labeling_trainer">labeling nodes</a> in graphs </li>
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<li>A large-scale <a href="ml.html#svm_rank_trainer">SVM-Rank</a> implementation</li>
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<li>An online <a href="ml.html#krls">kernel RLS regression</a> algorithm</li>
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<li>An online <a href="ml.html#svm_pegasos">SVM classification</a> algorithm</li>
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<li><a href="ml.html#vector_normalizer_frobmetric">Semidefinite Metric Learning</a></li>
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<li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector and
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offline support vector <a href="ml.html#svm_one_class_trainer">one-class classification</a></li>
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<li>Clustering algorithms: <a href="ml.html#find_clusters_using_kmeans">linear</a>
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or <a href="ml.html#kkmeans">kernel k-means</a>,
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<a href="ml.html#chinese_whispers">Chinese Whispers</a>, and
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<a href="ml.html#newman_cluster">Newman clustering</a>. </li>
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<li><a href="ml.html#rbf_network_trainer">Radial Basis Function Networks</a></li>
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<li><a href="ml.html#mlp">Multi layer perceptrons</a> </li>
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</ul>
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</li>
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<li><b>Numerical Algorithms</b>
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<ul>
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<li>A fast <a href="linear_algebra.html#matrix">matrix</a> object implemented using the expression
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templates technique and capable of using BLAS and LAPACK libraries when available.</li>
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<li>Numerous linear algebra and mathematical operations are defined for the matrix object such as the
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<a href="dlib/matrix/matrix_la_abstract.h.html#svd">singular value decomposition</a>,
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<a href="dlib/matrix/matrix_utilities_abstract.h.html#trans">transpose</a>,
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<a href="dlib/matrix/matrix_math_functions_abstract.h.html#sin">trig functions</a>, etc.</li>
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<li>General purpose unconstrained non-linear optimization algorithms using the
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<a href="optimization.html#cg_search_strategy">conjugate gradient</a>,
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<a href="optimization.html#bfgs_search_strategy">BFGS</a>, and
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<a href="optimization.html#lbfgs_search_strategy">L-BFGS</a>
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techniques</li>
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<li> <a href="optimization.html#solve_least_squares_lm">Levenberg-Marquardt</a> for solving non-linear
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least squares problems </li>
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<li>Box-constrained derivative-free optimization via the
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<a href="optimization.html#find_min_bobyqa">BOBYQA</a> algorithm</li>
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<li>An implementation of the <a href="optimization.html#oca">Optimized Cutting Plane Algorithm</a></li>
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<li><preserve_space><a href="optimization.html#solve_qp_using_smo">Several</a>
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<a href="optimization.html#solve_qp2_using_smo">quadratic</a>
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<a href="optimization.html#solve_qp3_using_smo">program</a>
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<a href="optimization.html#solve_qp4_using_smo">solvers</a></preserve_space> </li>
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<li>Combinatorial optimization tools for solving
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<a href="optimization.html#max_cost_assignment">optimal assignment</a> and
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<a href="optimization.html#min_cut">min cut/max flow</a> problems as well as
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the <a href="optimization.html#find_max_parse_cky">CKY algorithm</a> for finding the most probable parse tree</li>
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<li>A <a href="algorithms.html#bigint">big integer</a> object</li>
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<li>A <a href="algorithms.html#rand">random number</a> object</li>
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</ul>
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</li>
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<li><b>Graphical Model Inference Algorithms</b>
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<ul>
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<li><a href="bayes.html#bayesian_network_join_tree">Join tree</a> algorithm for exact inference in
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a Bayesian network.</li>
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<li><a href="bayes.html#bayesian_network_gibbs_sampler">Gibbs sampler</a> markov chain monte
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carlo algorithm for approximate inference in a Bayesian network.</li>
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<li>Routines for performing MAP inference in
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<a href="optimization.html#find_max_factor_graph_viterbi">chain-structured</a>,
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<a href="optimization.html#find_max_factor_graph_potts">Potts</a>, or
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<a href="optimization.html#find_max_factor_graph_nmplp">general</a> factor graphs.</li>
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</ul>
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</li>
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<li><b>Image Processing</b>
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<ul>
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<li>Routines for <a href="imaging.html#load_image">reading</a> and
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<a href="imaging.html#save_bmp">writing</a> common image formats. </li>
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<li>Automatic color space conversion between various pixel types</li>
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<li>Common image operations such as edge finding and morphological operations</li>
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<li>Implementations of the <a href="imaging.html#get_surf_points">SURF</a>,
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<a href="imaging.html#hog_image">HOG</a>, and <a href="imaging.html#extract_fhog_features">FHOG</a>
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feature extraction algorithms.</li>
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<li>Tools for <a href="imaging.html#object_detector">detecting objects</a> in images including
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<a href="imaging.html#get_frontal_face_detector">frontal face detection</a> and
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<a href="imaging.html#shape_predictor">object pose estimation</a>.</li>
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</ul>
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</li>
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<li><b>Threading</b>
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<ul>
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<li>The library provides a portable and simple <a href="api.html#threads">threading API</a></li>
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@ -98,96 +188,6 @@
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</li>
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<li><b>Numerical Algorithms</b>
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<ul>
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<li>A fast <a href="linear_algebra.html#matrix">matrix</a> object implemented using the expression
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templates technique and capable of using BLAS and LAPACK libraries when available.</li>
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<li>Numerous linear algebra and mathematical operations are defined for the matrix object such as the
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<a href="dlib/matrix/matrix_la_abstract.h.html#svd">singular value decomposition</a>,
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<a href="dlib/matrix/matrix_utilities_abstract.h.html#trans">transpose</a>,
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<a href="dlib/matrix/matrix_math_functions_abstract.h.html#sin">trig functions</a>, etc.</li>
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<li>General purpose unconstrained non-linear optimization algorithms using the
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<a href="optimization.html#cg_search_strategy">conjugate gradient</a>,
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<a href="optimization.html#bfgs_search_strategy">BFGS</a>, and
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<a href="optimization.html#lbfgs_search_strategy">L-BFGS</a>
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techniques</li>
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<li> <a href="optimization.html#solve_least_squares_lm">Levenberg-Marquardt</a> for solving non-linear
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least squares problems </li>
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<li>Box-constrained derivative-free optimization via the
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<a href="optimization.html#find_min_bobyqa">BOBYQA</a> algorithm</li>
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<li>An implementation of the <a href="optimization.html#oca">Optimized Cutting Plane Algorithm</a></li>
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<li><preserve_space><a href="optimization.html#solve_qp_using_smo">Several</a>
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<a href="optimization.html#solve_qp2_using_smo">quadratic</a>
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<a href="optimization.html#solve_qp3_using_smo">program</a>
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<a href="optimization.html#solve_qp4_using_smo">solvers</a></preserve_space> </li>
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<li>Combinatorial optimization tools for solving
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<a href="optimization.html#max_cost_assignment">optimal assignment</a> and
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<a href="optimization.html#min_cut">min cut/max flow</a> problems as well as
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the <a href="optimization.html#find_max_parse_cky">CKY algorithm</a> for finding the most probable parse tree</li>
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<li>A <a href="algorithms.html#bigint">big integer</a> object</li>
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<li>A <a href="algorithms.html#rand">random number</a> object</li>
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</ul>
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</li>
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<li><b>Machine Learning Algorithms</b>
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<ul>
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<li>Conventional SMO based Support Vector Machines for <a href="ml.html#svm_nu_trainer">classification</a>
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and <a href="ml.html#svr_trainer">regression</a> </li>
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<li>Reduced-rank methods for large-scale <a href="ml.html#svm_c_ekm_trainer">classification</a>
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and <a href="ml.html#krr_trainer">regression</a></li>
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<li>Relevance vector machines for <a href="ml.html#rvm_trainer">classification</a>
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and <a href="ml.html#rvm_regression_trainer">regression</a> </li>
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<li>General purpose <a href="ml.html#one_vs_one_trainer">multiclass classification</a> tools</li>
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<li>A <a href="ml.html#svm_multiclass_linear_trainer">Multiclass SVM</a></li>
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<li>A tool for solving the optimization problem associated with
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<a href="ml.html#structural_svm_problem">structural support vector machines</a>. </li>
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<li>Structural SVM tools for <a href="ml.html#structural_sequence_labeling_trainer">sequence labeling</a> </li>
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<li>Structural SVM tools for solving <a href="ml.html#structural_assignment_trainer">assignment problems</a> </li>
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<li>Structural SVM tools for <a href="ml.html#structural_object_detection_trainer">object detection</a> in images </li>
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<li>Structural SVM tools for <a href="ml.html#structural_graph_labeling_trainer">labeling nodes</a> in graphs </li>
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<li>A large-scale <a href="ml.html#svm_rank_trainer">SVM-Rank</a> implementation</li>
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<li>An online <a href="ml.html#krls">kernel RLS regression</a> algorithm</li>
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<li>An online <a href="ml.html#svm_pegasos">SVM classification</a> algorithm</li>
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<li><a href="ml.html#vector_normalizer_frobmetric">Semidefinite Metric Learning</a></li>
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<li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector and
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offline support vector <a href="ml.html#svm_one_class_trainer">one-class classification</a></li>
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<li>Clustering algorithms: <a href="ml.html#find_clusters_using_kmeans">linear</a>
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or <a href="ml.html#kkmeans">kernel k-means</a>,
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<a href="ml.html#chinese_whispers">Chinese Whispers</a>, and
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<a href="ml.html#newman_cluster">Newman clustering</a>. </li>
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<li><a href="ml.html#rbf_network_trainer">Radial Basis Function Networks</a></li>
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<li><a href="ml.html#mlp">Multi layer perceptrons</a> </li>
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</ul>
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</li>
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<li><b>Graphical Model Inference Algorithms</b>
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<ul>
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<li><a href="bayes.html#bayesian_network_join_tree">Join tree</a> algorithm for exact inference in
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a Bayesian network.</li>
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<li><a href="bayes.html#bayesian_network_gibbs_sampler">Gibbs sampler</a> markov chain monte
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carlo algorithm for approximate inference in a Bayesian network.</li>
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<li>Routines for performing MAP inference in
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<a href="optimization.html#find_max_factor_graph_viterbi">chain-structured</a>,
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<a href="optimization.html#find_max_factor_graph_potts">Potts</a>, or
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<a href="optimization.html#find_max_factor_graph_nmplp">general</a> factor graphs.</li>
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</ul>
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</li>
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<li><b>Image Processing</b>
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<ul>
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<li>Routines for <a href="imaging.html#load_image">reading</a> and
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<a href="imaging.html#save_bmp">writing</a> common image formats. </li>
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<li>Automatic color space conversion between various pixel types</li>
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<li>Common image operations such as edge finding and morphological operations</li>
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<li>Implementations of the <a href="imaging.html#get_surf_points">SURF</a>,
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<a href="imaging.html#hog_image">HOG</a>, and <a href="imaging.html#extract_fhog_features">FHOG</a>
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feature extraction algorithms.</li>
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<li>Tools for <a href="imaging.html#object_detector">detecting objects</a> in images including
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<a href="imaging.html#get_frontal_face_detector">frontal face detection</a> and
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<a href="imaging.html#shape_predictor">object pose estimation</a>.</li>
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</ul>
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</li>
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<li><b>Data Compression and Integrity Algorithms</b>
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<ul>
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<li>A <a href="algorithms.html#crc32">CRC 32</a> object</li>
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@ -31,13 +31,16 @@
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software components, each accompanied by extensive documentation and thorough debugging modes.
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</p>
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<p>
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Since development began in 2002, dlib has grown to include a wide
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variety of tools. In particular, it now contains software components
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for dealing with networking, threads, graphical interfaces, complex
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data structures, linear algebra, statistical machine learning, image
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processing, data mining, XML and text parsing, numerical
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optimization, Bayesian networks, and numerous other tasks. In
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<a href='mailto:davis@dlib.net'>Davis King</a> has been the primary
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author of dlib since development began in 2002. In that time
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dlib has grown to include a wide variety of tools. In particular,
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it now contains software components for dealing with networking,
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threads, graphical interfaces, complex data structures, linear
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algebra, statistical machine learning, image processing, data
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mining, XML and text parsing, numerical optimization, Bayesian
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networks, and numerous other tasks. In
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recent years, much of the development has been focused on creating
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a broad set of statistical machine learning tools. However, dlib
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remains a general purpose library and <a href="howto_contribute.html">welcomes contributions</a> of high
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@ -45,7 +48,7 @@
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</p>
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<p>
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Part of the development philosophy of dlib is a dedication to
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Core to the development philosophy of dlib is a dedication to
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portability and ease of use. Therefore, all code in dlib is designed
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to be as portable as possible and similarly to not require a user to
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configure or install anything. To help achieve this, all platform
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@ -91,19 +91,19 @@
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<name>Home</name>
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<link>http://dlib.net</link>
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</item>
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<item>
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<name>Dlib Blog</name>
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<link>http://blog.dlib.net</link>
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</item>
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<item>
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<name>Forums</name>
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<link>https://sourceforge.net/p/dclib/discussion</link>
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</item>
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<item>
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<name>Who uses dlib?</name>
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<link>http://sourceforge.net/p/dclib/wiki/Known_users/</link>
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</item>
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</web>
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<item>
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<name>Dlib Blog</name>
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<link>http://blog.dlib.net</link>
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</item>
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<item>
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<name>Forums</name>
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<link>https://sourceforge.net/p/dclib/discussion</link>
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</item>
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<item>
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||||
<name>Who uses dlib?</name>
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<link>http://sourceforge.net/p/dclib/wiki/Known_users/</link>
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</item>
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<item>
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<name>Introduction</name>
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