dlib ml 18.1

Davis King — Mon, 25 Mar 2013 23:48:23 -0000

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, 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.

mloss.org dlib ml

dlib ml 18.1