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Logo hapFabia 1.4.2

by hochreit - December 28, 2013, 17:24:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6316 views, 1232 downloads, 1 subscription

About: hapFabia is an R package for identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data. It detects 100 times smaller segments than previous methods.

Changes:

o citation update

o plot function improved


Logo yaplf 0.7

by malchiod - April 22, 2010, 11:34:07 CET [ Project Homepage BibTeX Download ] 4977 views, 1230 downloads, 1 subscription

About: yaplf (Yet Another Python Learning Framework) is an extensible machine learning framework written in python

Changes:

Initial Announcement on mloss.org.


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 ] 5757 views, 1222 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


Logo r-cran-ncvreg 2.5-0

by r-cran-robot - March 15, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 5477 views, 1216 downloads, 0 subscriptions

About: Regularization paths for SCAD- and MCP-penalized regression models

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.449164


Logo RLPy 1.3a

by bobklein2 - August 28, 2014, 14:34:35 CET [ Project Homepage BibTeX Download ] 5719 views, 1215 downloads, 1 subscription

About: RLPy is a framework for performing reinforcement learning (RL) experiments in Python. RLPy provides a large library of agent and domain components, and a suite of tools to aid in experiments (parallelization, hyperparameter optimization, code profiling, and plotting).

Changes:
  • Fixed bug where results using same random seed were different with visualization turned on/off
  • Created RLPy package on pypi (Available at https://pypi.python.org/pypi/rlpy)
  • Switched from custom logger class to python default
  • Added unit tests
  • Code readability improvements (formatting, variable names/ordering)
  • Restructured TD Learning heirarchy
  • Updated tutorials

Logo Large margin filtering 0.9

by rflamary - February 18, 2012, 15:50:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4821 views, 1214 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


Logo r-cran-caretNWS 0.25

by r-cran-robot - December 3, 2008, 00:00:00 CET [ Project Homepage BibTeX Download ] 4984 views, 1214 downloads, 1 subscription

About: Classification and Regression Training in Parallel Using NetworkSpaces: Augment some caret functions using parallel processing

Changes:

Initial Announcement on mloss.org.


Logo OpenANN 1.1.0

by afabisch - September 26, 2013, 23:52:03 CET [ Project Homepage BibTeX Download ] 6088 views, 1210 downloads, 2 subscriptions

About: A library for artificial neural networks.

Changes:

Added algorithms:

  • L-BFGS optimizer
  • k-means
  • sparse auto-encoder
  • preprocessing: normalization, PCA, ZCA whitening

Logo pySPACE 1.2

by krell84 - October 29, 2014, 15:36:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6216 views, 1208 downloads, 1 subscription

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.

Changes:

improved testing, improved documentation, windows compatibility, more algorithms


About: You can use the software in this package to efficiently sample from

Changes:

Initial Announcement on mloss.org.


Showing Items 381-390 of 628 on page 39 of 63: First Previous 34 35 36 37 38 39 40 41 42 43 44 Next Last