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Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 17272 views, 7436 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.


About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs).

Changes:

compilation problems fixed


Logo peewit 0.10

by lorenz - May 7, 2014, 16:04:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16995 views, 3342 downloads, 1 subscription

About: peewit provides services for programming, running and result examination of machine learning experiments. It does not include any ML algorithms, has no GUI, and presumes certain uniformity of the experimental layout. But it does not make assumptions on the type of task under study. The current version-number is 0.10.

Changes:

v-cube with side-cubes


Logo Cognitive Foundry 3.3.3

by Baz - May 21, 2013, 05:59:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16959 views, 2689 downloads, 2 subscriptions

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

Changes:
  • General:
    • Made code able to compile under both Java 1.6 and 1.7. This required removing some potentially unsafe methods that used varargs with generics.
    • Upgraded XStream dependency to 1.4.4.
    • Improved support for regression algorithms in learning.
    • Added general-purpose adapters to make it easier to compose learning algorithms and adapt their input or output.
  • Common Core:
    • Added isSparse, toArray, dotDivide, and dotDivideEquals methods for Vector and Matrix.
    • Added scaledPlus, scaledPlusEquals, scaledMinus, and scaledMinusEquals to Ring (and thus Vector and Matrix) for potentially faster such operations.
    • Fixed issue where matrix and dense vector equals was not checking for equal dimensionality.
    • Added transform, transformEquals, tranformNonZeros, and transformNonZerosEquals to Vector.
    • Made LogNumber into a signed version of a log number and moved the prior unsigned implementation into UnsignedLogNumber.
    • Added EuclideanRing interface that provides methods for times, timesEquals, divide, and divideEquals. Also added Field interface that provides methods for inverse and inverseEquals. These interfaces are now implemented by the appropriate number classes such as ComplexNumber, MutableInteger, MutableLong, MutableDouble, LogNumber, and UnsignedLogNumber.
    • Added interface for Indexer and DefaultIndexer implementation for creating a zero-based indexing of values.
    • Added interfaces for MatrixFactoryContainer and DivergenceFunctionContainer.
    • Added ReversibleEvaluator, which various identity functions implement as well as a new utility class ForwardReverseEvaluatorPair to create a reversible evaluator from a pair of other evaluators.
    • Added method to create an ArrayList from a pair of values in CollectionUtil.
    • ArgumentChecker now properly throws assertion errors for NaN values. Also added checks for long types.
    • Fixed handling of Infinity in subtraction for LogMath.
    • Fixed issue with angle method that would cause a NaN if cosine had a rounding error.
    • Added new createMatrix methods to MatrixFactory that initializes the Matrix with the given value.
    • Added copy, reverse, and isEmpty methods for several array types to ArrayUtil.
    • Added utility methods for creating a HashMap, LinkedHashMap, HashSet, or LinkedHashSet with an expected size to CollectionUtil.
    • Added getFirst and getLast methods for List types to CollectionUtil.
    • Removed some calls to System.out and Exception.printStackTrace.
  • Common Data:
    • Added create method for IdentityDataConverter.
    • ReversibleDataConverter now is an extension of ReversibleEvaluator.
  • Learning Core:
    • Added general learner transformation capability to make it easier to adapt and compose algorithms. InputOutputTransformedBatchLearner provides this capability for supervised learning algorithms by composing together a triplet. CompositeBatchLearnerPair does it for a pair of algorithms.
    • Added a constant and identity learners.
    • Added Chebyshev, Identity, and Minkowski distance metrics.
    • Added methods to DatasetUtil to get the output values for a dataset and to compute the sum of weights.
    • Made generics more permissive for supervised cost functions.
    • Added ClusterDistanceEvaluator for taking a clustering that encodes the distance from an input value to all clusters and returns the result as a vector.
    • Fixed potential round-off issue in decision tree splitter.
    • Added random subspace technique, implemented in RandomSubspace.
    • Separated functionality from LinearFunction into IdentityScalarFunction. LinearFunction by default is the same, but has parameters that can change the slope and offset of the function.
    • Default squashing function for GeneralizedLinearModel and DifferentiableGeneralizedLinearModel is now a linear function instead of an atan function.
    • Added a weighted estimator for the Poisson distribution.
    • Added Regressor interface for evaluators that are the output of (single-output) regression learning algorithms. Existing such evaluators have been updated to implement this interface.
    • Added support for regression ensembles including additive and averaging ensembles with and without weights. Added a learner for regression bagging in BaggingRegressionLearner.
    • Added a simple univariate regression class in UnivariateLinearRegression.
    • MultivariateDecorrelator now is a VectorInputEvaluator and VectorOutputEvaluator.
    • Added bias term to PrimalEstimatedSubGradient.
  • Text Core:
    • Fixed issue with the start position for tokens from LetterNumberTokenizer being off by one except for the first one.

Logo JMLR LIBLINEAR 1.32

by biconnect - September 3, 2008, 17:35:24 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16954 views, 1923 downloads, 2 subscriptions

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About: LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, L1-loss linear SVM, and multi-class SVM

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 ] 16923 views, 3837 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 MATLAB spectral clustering package 1.1

by wenyenc - February 4, 2010, 01:54:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16695 views, 3102 downloads, 1 subscription

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About: A MATLAB spectral clustering package to deal with large data sets. Our tool can handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. Spectral clustering algorithm has been [...]

Changes:
  • Add bib
  • Add code of nystrom without orthogonalization
  • Add accuracy quality measure
  • Add more running scripts

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 ] 16304 views, 1832 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 Accord.NET Framework 2.10.0

by cesarsouza - September 9, 2013, 16:12:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16105 views, 3287 downloads, 2 subscriptions

About: Accord.NET provides statistical analysis, machine learning, image processing and computer vision methods for .NET applications. The Accord.NET Framework extends the popular AForge.NET with new features, adding to a more complete environment for scientific computing in .NET.

Changes:

This release aimed to provide improvements to the documentation. Most of the Univariate Distributions now include proper examples for all main functions and measures in their summary page. Also, a wide set of imaging methods, such as Haralick's set of textural features, the Local Binary Pattern, Gabor, Kirsch, and Variance filters have been added. Also includes the Denavit-Hartenberg model for kinematic chains and many updates, optimizations, corrections and bug-fixes in all major namespaces.

For a complete list of changes, please see the full release notes at the release details page at:

https://github.com/accord-net/framework/releases


Logo gensim 0.8.6

by Radim - December 9, 2012, 13:15:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15717 views, 3286 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 41-50 of 550 on page 5 of 55: Previous 1 2 3 4 5 6 7 8 9 10 Next Last