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Logo Variational Dirichlet process Gaussian mixtures 0.1

by kenkurihara - April 22, 2008, 01:41:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8357 views, 1784 downloads, 0 subscriptions

About: This is an implementation of variational Dirichlet process Gaussian mixtures. Thus, this works like the k-means, but it searched for the number of clusters as well. Couple algorithms are [...]

Changes:

Initial Announcement on mloss.org.


Logo HLearn 1.0

by mikeizbicki - May 9, 2013, 05:58:18 CET [ Project Homepage BibTeX Download ] 7217 views, 1781 downloads, 1 subscription

About: HLearn makes simple machine learning routines available in Haskell by expressing them according to their algebraic structure

Changes:

Updated to version 1.0


Logo Correlative Matrix Mapping, CMM 1.1

by emstrick - July 5, 2011, 15:15:21 CET [ BibTeX BibTeX for corresponding Paper Download ] 8058 views, 1776 downloads, 1 subscription

About: Correlative Matrix Mapping (CMM) provides a supervised linear data mapping into a Euclidean subspace of given dimension. Applications include denoising, visualization, label-specific data preprocessing, and assessment of data attribute pairs relevant for the supervised mapping. Solving auto-association problems yields linear multidimensional scaling, similar to PCA, but usually with more faithful low-dimensional mappings.

Changes:

Tue Jul 5 14:40:03 CEST 2011 - Bugfixes and cleanups

  • single precision data affected pinv(). Now fairer using double precision.
  • early stopping did not work properly; now fixed
  • Hessian update mode globally controlled via hessmode, 'lbfgs' / 'bfgs'
  • distmat.m corrected for rounding problems and extended to distmat(X,Y)
  • replaced files: corv.m + corvgrad.m -> corvg.m
  • removed unused files: corrmat.m, splitdata.m, traforankapply.m

Logo BenchMarking Via Weka 0.0.4

by fracpete - December 4, 2008, 01:15:15 CET [ Project Homepage BibTeX Download ] 9823 views, 1768 downloads, 0 comments, 2 subscriptions

About: BenchMarking Via Weka is a client-server architecture that supports interoperability between different machine learning systems. Machine learning systems need to provide mechanisms for processing [...]

Changes:

Initial Announcement on mloss.org.


Logo Logistic regression with dual spectral regularization 1.0

by ryota - April 27, 2008, 08:44:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7417 views, 1767 downloads, 1 subscription

About: It solves a classification problem over symmetric matrices with dual spectral norm (trace norm) regularization using a simple interior point method. It was successfully applied to single trial EEG [...]

Changes:

Initial Announcement on mloss.org.


Logo Linear SVM with general regularization 1.0

by rflamary - October 5, 2012, 15:34:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6345 views, 1756 downloads, 1 subscription

About: This package is an implementation of a linear svm solver with a wide class of regularizations on the svm weight vector (l1, l2, mixed norm l1-lq, adaptive lasso). We provide solvers for the classical single task svm problem and for multi-task with joint feature selection or similarity promoting term.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-grplasso 0.4-2

by r-cran-robot - April 1, 2009, 00:00:00 CET [ Project Homepage BibTeX Download ] 8488 views, 1750 downloads, 1 subscription

About: Fitting user specified models with Group Lasso penalty

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.428021


About: Matlab code for semi-supervised regression and dimensionality reduction using Hessian energy.

Changes:

Initial Announcement on mloss.org.


Logo TurboParser 2.0

by afm - October 11, 2012, 02:59:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7954 views, 1727 downloads, 1 subscription

About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar.

Changes:

This version introduces a number of new features:

  • The parser does not depend anymore on CPLEX (or any other non-free LP solver). Instead, the decoder is now based on AD3, our free library for approximate MAP inference.

  • The parser now outputs dependency labels along with the backbone structure.

  • As a bonus, we now provide a trainable part-of-speech tagger, called TurboTagger, which can be used in standalone mode, or to provide part-of-speech tags as input for the parser. TurboTagger has state-of-the-art accuracy for English (97.3% on section 23 of the Penn Treebank) and is fast (~40,000 tokens per second).

  • The parser is much faster than in previous versions. You may choose among a basic arc-factored parser (~4,300 tokens per second), a standard second-order model with consecutive sibling and grandparent features (the default; ~1,200 tokens per second), and a full model with head bigram and arbitrary sibling features (~900 tokens per second).

Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package.

This software has been tested on Linux, but it should run in other platforms with minor adaptations.


Logo Nen Beta

by pascal - February 19, 2012, 00:31:34 CET [ Project Homepage BibTeX Download ] 7125 views, 1725 downloads, 1 subscription

About: 3-layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM.

Changes:

Initial Announcement on mloss.org.


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