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Logo seqan 1.2

by sonne - November 2, 2009, 14:54:08 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6607 views, 1274 downloads, 1 subscription

About: SeqAn is an open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data.

Changes:
  • 5 more applications, i.e. DFI, MicroRazerS, PairAlign, SeqCons, TreeRecon
  • stable release of RazerS supporting paired-end read mapping and configurable sensitivity
  • new alignment algorithms, e.g. banded, configurable alignments (overlap, semi-global, ...)
  • realignment algorithm
  • NGS data structures and formats, e.g. SAM, Amos, ...
  • new alphabets, e.g. Dna with base call qualities, profile characters
  • auxiliary data structures and algorithms, e.g. double ended queue, command line parser
  • positional scores
  • CMake support

Logo JMLR Shark 2.3.0

by igel - October 24, 2009, 22:12:48 CET [ Project Homepage BibTeX Download ] 27003 views, 5453 downloads, 1 subscription

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About: SHARK is a modular C++ library for the design and optimization of adaptive systems. It provides various machine learning and computational intelligence techniques.

Changes:
  • new build system
  • minor bug fixes

Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 18033 views, 7640 downloads, 2 subscriptions

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About: Elefant is an open source software platform for the Machine Learning community licensed under the Mozilla Public License (MPL) and developed using Python, C, and C++. We aim to make it the platform [...]

Changes:

This release contains the Stream module as a first step in the direction of providing C++ library support. Stream aims to be a software framework for the implementation of large scale online learning algorithms. Large scale, in this context, should be understood as something that does not fit in the memory of a standard desktop computer.

Added Bundle Methods for Regularized Risk Minimization (BMRM) allowing to choose from a list of loss functions and solvers (linear and quadratic).

Added the following loss classes: BinaryClassificationLoss, HingeLoss, SquaredHingeLoss, ExponentialLoss, LogisticLoss, NoveltyLoss, LeastMeanSquareLoss, LeastAbsoluteDeviationLoss, QuantileRegressionLoss, EpsilonInsensitiveLoss, HuberRobustLoss, PoissonRegressionLoss, MultiClassLoss, WinnerTakesAllMultiClassLoss, ScaledSoftMarginMultiClassLoss, SoftmaxMultiClassLoss, MultivariateRegressionLoss

Graphical User Interface provides now extensive documentation for each component explaining state variables and port descriptions.

Changed saving and loading of experiments to XML (thereby avoiding storage of large input data structures).

Unified automatic input checking via new static typing extending Python properties.

Full support for recursive composition of larger components containing arbitrary statically typed state variables.


Logo JMLR RL Glue and Codecs -- Glue 3.x and Codecs

by btanner - October 12, 2009, 07:50:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17068 views, 1929 downloads, 1 subscription

About: RL-Glue allows agents, environments, and experiments written in Java, C/C++, Matlab, Python, and Lisp to inter operate, accelerating research by promoting software re-use in the community.

Changes:

RL-Glue paper has been published in JMLR.


Logo Online Random Forests 0.11

by amirsaffari - October 3, 2009, 17:25:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8479 views, 1479 downloads, 1 subscription

About: This package implements the “Online Random Forests” (ORF) algorithm of Saffari et al., ICCV-OLCV 2009. This algorithm extends the offline Random Forests (RF) to learn from online training data samples. ORF is a multi-class classifier which is able to learn the classifier without 1-vs-all or 1-vs-1 binary decompositions.

Changes:

Initial Announcement on mloss.org.


Logo EANT Without Structural Optimization 1.0

by yk - September 28, 2009, 12:34:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4692 views, 1649 downloads, 1 subscription

About: EANT Without Structural Optimization is used to learn a policy in either complete or partially observable reinforcement learning domains of continuous state and action space.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-VR 7.2-49

by r-cran-robot - September 25, 2009, 00:00:00 CET [ Project Homepage BibTeX Download ] 9653 views, 2506 downloads, 1 subscription

About: VR

Changes:

Fetched by r-cran-robot on 2009-10-03 07:16:05.643423


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 ] 4223 views, 869 downloads, 1 subscription

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

Changes:

Initial Announcement on mloss.org.


Logo MALLET 2.0-rc4

by jacktanner - August 24, 2009, 23:10:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11325 views, 1813 downloads, 1 subscription

About: MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to [...]

Changes:

MALLET 2.0 RC4 Release Notes July 16, 2009

Major updates:

An implementation of generalized expectation criteria training of MaxEnt classifiers and methods for obtaining constraints (c.f. Gregory Druck, Gideon Mann, Andrew McCallum "Learning from Labeled Features using Generalized Expectation Criteria.")

PagedInstanceList has been substantially rewritten by Mike Bond.

Bug fixes to topic model hyperparameter optimization and topic inference.


Logo JMLR Java Machine Learning Library 0.1.5

by thomas - August 20, 2009, 23:47:45 CET [ Project Homepage BibTeX Download ] 19899 views, 2813 downloads, 1 subscription

About: Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists.

Changes:

new release


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