Project details for dlib ml

Logo JMLR dlib ml 17.43

by davis685 - October 23, 2011, 13:26:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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:

  • support vector machines for classification and regression
  • 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)
  • 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 contains a number of new features and bug fixes. Some highlights are:

  • A structural support vector machine method for learning to detect objects in images.
  • Two new tools for finding the most likely assignment in a graphical model. One for chain-structured graphs and the other for approximate inference in general graphs.
BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Linux, Macosx, Windows, Unix, Solaris
Data Formats: Svmlight, Binary
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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