Project details for dlib ml

Logo JMLR dlib ml 19.5

by davis685 - August 28, 2017, 01:54:41 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:

  • 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 brings a lot of new features to dlib. There is a dlib to caffe converter, a bunch of new deep learning layer types, cuDNN v6 and v7 support, and a bunch of optimizations that make things run faster in different situations, like ARM NEON support, which makes HOG based detectors run a lot faster on mobile devices. However, the coolest new feature is an upgrade to the CNN+MMOD object detector to support detecting objects with varying aspect ratios.

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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