Description:

A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

The library provides efficient implementations of the following algorithms:

• Deep neural networks
• support vector machines for classification, regression, and ranking
• reduced-rank methods for large-scale classification and regression.
  This includes an SVM implementation and a method for performing kernel ridge regression with efficient LOO cross-validation.
• multi-class SVM
• structural SVM (modes: single-threaded, multi-threaded, and fully distributed)
• sequence labeling using structured SVMs
• relevance vector machines for regression and classification
• reduced set approximation of SV decision surfaces
• online kernel RLS regression
• online kernelized centroid estimation/one class classifier
• online SVM classification
• kernel k-means clustering
• radial basis function networks
• kernelized recursive feature ranking
• Bayesian network inference using junction trees or MCMC
• General purpose unconstrained non-linear optimization algorithms using the conjugate gradient, BFGS, and L-BFGS techniques
• Levenberg-Marquardt for solving non-linear least squares problems
• A general purpose cutting plane optimizer.

The library also comes with extensive documentation and example programs that walk the user through the use of these machine learning techniques.

Finally, dlib includes a fast matrix library that lets the user use a simple Matlab like syntax. It is also capable of using BLAS and LAPACK libraries such as ATLAS or the Intel MKL when available. Additionally, the use of BLAS and LAPACK is transparent to the user, that is, the dlib matrix object uses BLAS and LAPACK internally to optimize various operations while still allowing the user to use a simple MATLAB like syntax.

Changes to previous version:

This release includes a lot of bug fixes, usability enhancements, and speedups. It also includes a new global optimization algorithm as well as new examples showing how to do semantic segmentation using dlib's deep learning tooling.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows, Unix, Solaris

Data Formats: Svmlight, Binary, Csv

Tags: Svm, Classification, Clustering, Regression, Kernel Methods, Matrix Library, Kkmeans, Optimization, Algorithms, Exact Bayesian Methods, Approximate Inference, Bayesian Networks, Junction Tree

Archive: download here

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