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Logo Large margin filtering 0.9

by rflamary - February 18, 2012, 15:50:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2545 views, 550 downloads, 1 subscription

About: Matlab SVM toolbox for learning large margin filters in signal or images.

Changes:

Initial Announcement on mloss.org.


Logo LASVM 1.1

by leonbottou - August 3, 2009, 15:50:30 CET [ Project Homepage BibTeX Download ] 8608 views, 1515 downloads, 0 subscriptions

About: Reference implementation of the LASVM online and active SVM algorithms as described in the JMLR paper. The interesting bit is a small C library that implements the LASVM process and reprocess [...]

Changes:

Minor bug fix


Logo Latent Topic Models for Hypertext 1.0

by amitg - September 2, 2009, 15:40:42 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3889 views, 801 downloads, 1 subscription

About: Source code for EM approximate learning in the Latent Topic Hypertext Model.

Changes:

Initial Announcement on mloss.org.


Logo Layer Based Dependency Parser 1.0.0

by openpr_nlpr - December 2, 2011, 04:51:23 CET [ Project Homepage BibTeX Download ] 1060 views, 339 downloads, 1 subscription

About: LDPar is an efficient data-driven dependency parser. You can train your own parsing model on treebank data and parse new data using the induced model.

Changes:

Initial Announcement on mloss.org.


About: Kaiye Wang, Ran He, Wei Wang, Liang Wang, Tiuniu Tan. Learning Coupled Feature Spaces for Cross-modal Matching. In ICCV, 2013.

Changes:

Initial Announcement on mloss.org.


Logo Learning the Kernel Matrix 1

by chap - January 14, 2008, 08:50:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5893 views, 1278 downloads, 1 subscription

About: Code for automatically selecting the kernel parameters of an SVM. It is based on a gradient descent minimization of either the radius/margin bound, the leave-one-out error, a validation error or the [...]

Changes:

Initial Announcement on mloss.org.


Logo LHOTSE 0.14

by mseeger - November 26, 2007, 21:12:19 CET [ Project Homepage BibTeX ] 3247 views, 27 downloads, 0 comments, 0 subscriptions

About: *LHOTSE* is a C++ class library designed for the implementation of large, efficient scientific applications in Machine Learning and Statistics.

Changes:

Initial Announcement on mloss.org.


Logo libAGF 0.9.7

by Petey - April 15, 2014, 04:55:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8316 views, 1670 downloads, 1 subscription

About: C++ software for statistical classification, probability estimation and interpolation/non-linear regression using variable bandwidth kernel estimation.

Changes:

New in Version 0.9.7:

  • multi-class classification generalizes class-borders algorithm using a recursive control language
  • hierarchical clustering
  • improved pre-processing

Logo LibBi 1.0.0

by lawmurray - June 23, 2013, 09:04:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1440 views, 351 downloads, 1 subscription

About: Bayesian state-space modelling and inference on high-performance computer hardware.

Changes:

Initial Announcement on mloss.org.


Logo libcmaes 0.9.1

by beniz - October 9, 2014, 10:08:18 CET [ Project Homepage BibTeX Download ] 1708 views, 360 downloads, 3 subscriptions

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.

Changes:

Small release with two bug fixes and tiny changes otherwise:

  • small API improvements

  • fixed bug in tolX stopping criteria when using 'sep' algorithm

  • fixed bug to the natural gradient with genotype /phenotype transforms

  • file stream now outputs parameter's mean in phenotype

  • tiny wrapper to simplify maximization of objective function (default is minimization)


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