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Logo FABIA 2.8.0

by hochreit - October 18, 2013, 10:14:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 10298 views, 2157 downloads, 1 subscription

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About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing.

Changes:

CHANGES IN VERSION 2.8.0

NEW FEATURES

o rescaling of lapla
o extractPlot does not plot sorted matrices

CHANGES IN VERSION 2.4.0

o spfabia bugfixes

CHANGES IN VERSION 2.3.1

NEW FEATURES

o Getters and setters for class Factorization

2.0.0:

  • spfabia: fabia for a sparse data matrix (in sparse matrix format) and sparse vector/matrix computations in the code to speed up computations. spfabia applications: (a) detecting >identity by descent< in next generation sequencing data with rare variants, (b) detecting >shared haplotypes< in disease studies based on next generation sequencing data with rare variants;
  • fabia for non-negative factorization (parameter: non_negative);
  • changed to C and removed dependencies to Rcpp;
  • improved update for lambda (alpha should be smaller, e.g. 0.03);
  • introduced maximal number of row elements (lL);
  • introduced cycle bL when upper bounds nL or lL are effective;
  • reduced computational complexity;
  • bug fixes: (a) update formula for lambda: tighter approximation, (b) corrected inverse of the conditional covariance matrix of z;

1.4.0:

  • New option nL: maximal number of biclusters per row element;
  • Sort biclusters according to information content;
  • Improved and extended preprocessing;
  • Update to R2.13

Logo factorie 1.0.0-M7

by apassos - October 7, 2013, 23:10:37 CET [ Project Homepage BibTeX Download ] 1788 views, 371 downloads, 1 subscription

About: [FACTORIE](http://factorie.cs.umass.edu) is a toolkit for deployable probabilistic modeling, implemented as a software library in [Scala](http://scala-lang.org). It provides its users with a succinct language for creating [factor graphs](http://en.wikipedia.org/wiki/Factor_graph), estimating parameters and performing inference. It also has implementations of many machine learning tools and a full NLP pipeline.

Changes:

Initial Announcement on mloss.org.


Logo JMLR fastclime 1.2.3

by colin1898 - March 10, 2014, 08:54:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2288 views, 616 downloads, 1 subscription

About: The package "fastclime" provides a method of recover the precision matrix efficiently by applying parametric simplex method. The computation is based on a linear optimization solver. It also contains a generic LP solver and a parameterized LP solver using parametric simplex method.

Changes:

Initial Announcement on mloss.org.


Logo fastICA 0.1

by maitra - February 28, 2013, 06:30:20 CET [ Project Homepage BibTeX Download ] 1876 views, 449 downloads, 1 subscription

About: The open-source C-package fastICA implements the fastICA algorithm of Aapo Hyvarinen et al. (URL: http://www.cs.helsinki.fi/u/ahyvarin/) to perform Independent Component Analysis (ICA) and Projection Pursuit. fastICA is released under the GNU Public License (GPL).

Changes:

Initial Announcement on mloss.org.


Logo JMLR FastInf 1.0

by arielj - June 4, 2010, 14:04:37 CET [ Project Homepage BibTeX Download ] 8474 views, 2909 downloads, 1 subscription

About: The library is focused on implementation of propagation based approximate inference methods. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm.

Changes:

Initial Announcement on mloss.org.


Logo FEAST 1.1.1

by apocock - June 30, 2014, 01:30:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20120 views, 4417 downloads, 1 subscription

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About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF. Written for C/C++ & Matlab.

Changes:
  • Bug fixes to memory management.
  • Compatibility changes for PyFeast python wrapper (note the C library now returns feature indices starting from 0, the Matlab wrapper still returns indices starting from 1).
  • Added C version of MIM.
  • Updated internal version of MIToolbox.

Logo fertilized forests 1.0beta

by Chrisl_S - January 23, 2015, 16:04:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 562 views, 98 downloads, 1 subscription

About: The fertilized forests project has the aim to provide an easy to use, easy to extend, yet fast library for decision forests. It summarizes the research in this field and provides a solid platform to extend it. Offering consistent interfaces to C++, Python and Matlab and being available for all major compilers gives the user high flexibility for using the library.

Changes:

Initial Announcement on mloss.org.


Logo figtree 0.9.2

by vmorariu - January 17, 2009, 00:13:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4450 views, 4011 downloads, 1 subscription

About: A library for fast computation of Gauss transforms in multiple dimensions, using the Improved Fast Gauss Transform and a tree data structure. This library is useful for efficient Kernel Density [...]

Changes:

Initial Announcement on mloss.org.


Logo Figue 1.0.1

by lerhumcbon - August 17, 2010, 04:03:35 CET [ Project Homepage BibTeX Download ] 3526 views, 848 downloads, 1 subscription

About: A collection of clustering algorithms implemented in Javascript.

Changes:

Initial Announcement on mloss.org.


Logo Finding nonlinear and stochastic structures in time series 1

by Dante - October 29, 2008, 11:14:44 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6219 views, 1231 downloads, 2 subscriptions

About: The Delay vector variance (DVV) method uses predictability of the signal in phase space to characterize the time series. Using the surrogate data methodology, so called DVV plots and DVV scatter [...]

Changes:

Initial Announcement on mloss.org.


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