All entries.
Showing Items 51-60 of 622 on page 6 of 63: Previous 1 2 3 4 5 6 7 8 9 10 11 Next Last

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 ] 24048 views, 4580 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 Java Machine Learning Library 0.1.5

by thomas - August 20, 2009, 23:47:45 CET [ Project Homepage BibTeX Download ] 23604 views, 3212 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


Logo JMLR Jstacs 2.2

by keili - February 17, 2016, 11:57:56 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22756 views, 5311 downloads, 3 subscriptions

About: A Java framework for statistical analysis and classification of biological sequences

Changes:

New classes and packages:

  • CorreationCoefficient: PerformanceMeasure
  • de.jstacs.clustering: package with classes for hierarchical clustering
  • DeBruijnGraphSequenceGenerator and DeBruijnSequenceGenerator for generating De Buijn sequences
  • CyclicSequenceAdaptor for representing cyclic sequences
  • PlotGeneratorResult for representing results that plot images to a Graphics2D object
  • TextResult for results that may be stored as text files
  • package de.jstacs.results.savers for generic classes that store results to disk
  • LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder for sparse local inhomogeneous mixture (Slim) models
  • PFMWrapperTrainSM for representing position frequency matrices and position weight matrices from databases
  • package de.jstacs.tools with classes for generic Jstacs tools that may be used in different user interfaces (command line, Galaxy, JavaFX)
  • Compression for ZIP compression of Strings
  • package de.jstacs.utils.graphics with generic GraphicsAdaptor using Apache XML commons
  • projects: Dimont, GeMoMa, Slim, TALEN, motif comparison

New features and improvements:

  • Major restructuring of Alignment for better efficiency
  • Alignment Costs and StringAlignment now Storable
  • New constructor of DataSet allowing a specified percentage of sequences to mismatch the given alphabet
  • BioJavaAdapter ported to BioJava 1.9
  • XMLParser now also allows for storing Sequences
  • New method for parsing HMMer profile HMMs in HMMFactory
  • Several minor improvements and bugfixes in many classes
  • Improvements of documentation of several classes

Logo MDP Modular toolkit for Data Processing 3.3

by otizonaizit - October 4, 2012, 15:17:33 CET [ Project Homepage BibTeX Download ] 22563 views, 5614 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 3 votes)

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 gensim 0.8.6

by Radim - December 9, 2012, 13:15:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22488 views, 4869 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)

Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 21893 views, 8498 downloads, 2 subscriptions

Rating Whole StarWhole Star1/2 StarEmpty StarEmpty Star
(based on 2 votes)

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 MSVMpack 1.5

by lauerfab - July 3, 2014, 16:02:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 21645 views, 6635 downloads, 2 subscriptions

About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.

Changes:
  • Windows binaries are now included (by Emmanuel Didiot)
  • MSVMpack can now be compiled on Windows (by Emmanuel Didiot)
  • Fixed polynomial kernel
  • Minor bug fixes

Logo r-cran-penalized 0.9-42

by r-cran-robot - November 6, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 21641 views, 4892 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 Apache Mahout 0.11.1

by gsingers - November 9, 2015, 16:12:06 CET [ Project Homepage BibTeX Download ] 21629 views, 5617 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 JMLR CARP 3.3

by volmeln - November 7, 2013, 15:48:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 21550 views, 6759 downloads, 1 subscription

About: CARP: The Clustering Algorithms’ Referee Package

Changes:

Generalized overlap error and some bugs have been fixed


Showing Items 51-60 of 622 on page 6 of 63: Previous 1 2 3 4 5 6 7 8 9 10 11 Next Last