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About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others.

Changes:
  • Modified specification of optional parameters (using sfo_opt)
  • Added sfo_ls_lazy for maximizing nonnegative submodular functions
  • Added sfo_fn_infogain, sfo_fn_lincomb, sfo_fn_invert, ...
  • Added additional documentation and more examples
  • Now Octave ready

Logo Apache Mahout 0.11.1

by gsingers - November 9, 2015, 16:12:06 CET [ Project Homepage BibTeX Download ] 29739 views, 7114 downloads, 3 subscriptions

About: Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm [...]

Changes:

Apache Mahout introduces a new math environment we call Samsara, for its theme of universal renewal. It reflects a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara is here to help people create their own math while providing some off-the-shelf algorithm implementations. At its core are general linear algebra and statistical operations along with the data structures to support them. You can use is as a library or customize it in Scala with Mahout-specific extensions that look something like R. Mahout-Samsara comes with an interactive shell that runs distributed operations on a Spark cluster. This make prototyping or task submission much easier and allows users to customize algorithms with a whole new degree of freedom. Mahout Algorithms include many new implementations built for speed on Mahout-Samsara. They run on Spark 1.3+ and some on H2O, which means as much as a 10x speed increase. You’ll find robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering. The new spark-itemsimilarity enables the next generation of cooccurrence recommenders that can use entire user click streams and context in making recommendations.


Logo BayesOpt, a Bayesian Optimization toolbox 0.8.2

by rmcantin - December 9, 2015, 04:53:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 40255 views, 7091 downloads, 4 subscriptions

About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python.

Changes:

-Fixed bug in save/restore. -Fixed bug in initial design.


Logo JMLR Shark 2.3.0

by igel - October 24, 2009, 22:12:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 37873 views, 6978 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:
  • moved to GitHub
  • new build system
  • minor bug fixes

Logo MDP Modular toolkit for Data Processing 3.3

by otizonaizit - October 4, 2012, 15:17:33 CET [ Project Homepage BibTeX Download ] 30383 views, 6935 downloads, 1 subscription

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About: MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

Changes:

What's new in version 3.3?

  • support sklearn versions up to 0.12
  • cleanly support reload
  • fail gracefully if pp server does not start
  • several bug-fixes and improvements

Logo JMLR CAM Java 3.1

by wangny - October 14, 2013, 22:46:03 CET [ Project Homepage BibTeX Download ] 18410 views, 6911 downloads, 1 subscription

About: The CAM R-Java software provides a noval way to solve blind source separation problem.

Changes:

In this version, we fix the problem of not working under newest R version R-3.0.


Logo Cognitive Foundry 3.4.2

by Baz - October 30, 2015, 06:53:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 40127 views, 6801 downloads, 4 subscriptions

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

Changes:
  • General:
    • Upgraded MTJ to 1.0.3.
  • Common:
    • Added package for hash function computation including Eva, FNV-1a, MD5, Murmur2, Prime, SHA1, SHA2
    • Added callback-based forEach implementations to Vector and InfiniteVector, which can be faster for iterating through some vector types.
    • Optimized DenseVector by removing a layer of indirection.
    • Added method to compute set of percentiles in UnivariateStatisticsUtil and fixed issue with percentile interpolation.
    • Added utility class for enumerating combinations.
    • Adjusted ScalarMap implementation hierarchy.
    • Added method for copying a map to VectorFactory and moved createVectorCapacity up from SparseVectorFactory.
    • Added method for creating square identity matrix to MatrixFactory.
    • Added Random implementation that uses a cached set of values.
  • Learning:
    • Implemented feature hashing.
    • Added factory for random forests.
    • Implemented uniform distribution over integer values.
    • Added Chi-squared similarity.
    • Added KL divergence.
    • Added general conditional probability distribution.
    • Added interfaces for Regression, UnivariateRegression, and MultivariateRegression.
    • Fixed null pointer exception that can happen in K-means with an empty cluster.
    • Fixed name of maxClusters property on AgglomerativeClusterer (was called maxMinDistance).
  • Text:
    • Improvements to LDA Gibbs sampler.

Logo r-cran-penalized 0.9-42

by r-cran-robot - November 6, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 31879 views, 6797 downloads, 1 subscription

About: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.939105


Logo MIToolbox 3.0.1

by apocock - March 2, 2017, 00:38:52 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 40870 views, 6771 downloads, 3 subscriptions

About: A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, Renyi entropy/mutual information, and weighted variants of Shannon entropies/mutual informations. Works with discrete distributions, and expects column vectors of features.

Changes:

Fixed a Windows compilation bug. MIToolbox v3 should now compile using Visual Studio.


Logo gensim 0.8.6

by Radim - December 9, 2012, 13:15:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 33219 views, 6724 downloads, 1 subscription

About: Python Framework for Vector Space Modelling that can handle unlimited datasets (streamed input, online algorithms work incrementally in constant memory).

Changes:
  • added the "hashing trick" (by Homer Strong)
  • support for adding target classes in SVMlight format (by Corrado Monti)
  • fixed problems with global lemmatizer object when running in parallel on Windows
  • parallelization of Wikipedia processing + added script version that lemmatizes the input documents
  • added class method to initialize Dictionary from an existing corpus (by Marko Burjek)

Showing Items 51-60 of 674 on page 6 of 68: Previous 1 2 3 4 5 6 7 8 9 10 11 Next Last