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Logo JMLR Mulan 1.5.0

by lefman - February 23, 2015, 21:19:05 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 52822 views, 14074 downloads, 0 subscriptions

About: Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions.

Changes:

Learners

  • MLCSSP.java: Added the MLCSSP algorithm (from ICML 2013)
  • Enhancements of multi-target regression capabilities
  • Improved CLUS support
  • Added pairwise classifier and pairwise transformation

Measures/Evaluation

  • Providing training data in the Evaluator is unnecessary in the case of specific measures.
  • Examples with missing ground truth are not skipped for measures that handle missing values.
  • Added logistics and squared error losses and measures

Bug fixes

  • IndexOutOfBounds in calculation of MiAP and GMiAP
  • Bug fix in Rcut.java
  • When in rank/score mode the meta-data contained additional unecessary attributes. (Newton Spolaor)

API changes

  • Upgrade to Java 7
  • Upgrade to Weka 3.7.10

Miscalleneous

  • Small changes and improvements in the wrapper classes for the CLUS library
  • ENTCS13FeatureSelection.java (new experiment)
  • Enumeration is now used for specifying the type of meta-data. (Newton Spolaor)

Logo r-cran-penalized 0.9-42

by r-cran-robot - November 6, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 57484 views, 13941 downloads, 0 subscriptions

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 KeLP 2.2.2

by kelpadmin - February 1, 2018, 00:57:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 56471 views, 13929 downloads, 0 subscriptions

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate prediction models without writing a single line of code.

Changes:

In addition to minor improvements and bug fixes, this release includes:

  • The possibility to generate the Compositional GRCT and the Compositional LCT data structures in kelp-input-generator.

  • New metrics for evaluating Classification Tasks.

  • New Tutorial and Unit Tests.

Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.2.2!


Logo r-cran-glmnet 1.9-3

by r-cran-robot - March 1, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 56358 views, 13789 downloads, 0 subscriptions

About: Lasso and elastic-net regularized generalized linear models

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.081872


Logo Cognitive Foundry 3.4.2

by Baz - October 30, 2015, 06:53:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 69231 views, 13701 downloads, 0 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 JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 84156 views, 13668 downloads, 0 subscriptions

About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine.

Changes:

Major changes :

  • The “early stopping” feature can now based on any metric output with the --outputinfo command line argument.

  • Early stopping now works with --slowresume command line argument.

Minor fixes:

  • More informative output when testing.

  • Various compilation glitch with recent clang (OsX/Linux).


Logo JMLR The Generalised Linear Models Inference and Estimation Toolbox 1.5

by hn - November 8, 2013, 13:58:03 CET [ Project Homepage BibTeX Download ] 55753 views, 13626 downloads, 0 subscriptions

About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.

Changes:

added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes

generalised non-Gaussian potentials so that affine instead of linear functions of the latent variables can be used


Logo KeBABS 1.5.4

by UBod - July 28, 2017, 09:55:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 69567 views, 13601 downloads, 0 subscriptions

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About: Kernel-Based Analysis of Biological Sequences

Changes:
  • importing apcluster package for avoiding method clashes
  • improved and completed change history in inst/NEWS and package vignette

Logo JMLR Jstacs 2.3

by keili - September 13, 2017, 14:25:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 60098 views, 13282 downloads, 0 subscriptions

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

Changes:

New classes and packages:

  • Jstacs 2.3 is the first release to be accompanied by JstacsFX, a library for building JavaFX-based graphical user interfaces based on JstacsTools
  • new interface MultiThreadedFunction
  • new class LargeSequenceReader for reading large sequence files in chunks
  • new interface QuickScanningSequenceScore
  • new class RegExpValidator for checking String inputs against a regular expression
  • new class IUPACDNAAlphabet

New features and improvements:

  • Alignments may now handle different costs for insert and delete gaps
  • ListResults may now be constructed from Collections of ResultSets
  • Several minor improvements and bugfixes in many classes
  • Improvements of documentation of several classes

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 ] 66608 views, 12917 downloads, 0 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 CARP 3.3

by volmeln - November 7, 2013, 15:48:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 46771 views, 12724 downloads, 0 subscriptions

About: CARP: The Clustering Algorithms’ Referee Package

Changes:

Generalized overlap error and some bugs have been fixed


Logo gensim 0.8.6

by Radim - December 9, 2012, 13:15:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 56454 views, 12490 downloads, 0 subscriptions

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 LIBOL 0.3.0

by stevenhoi - December 12, 2013, 15:26:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 38653 views, 12366 downloads, 0 subscriptions

About: LIBOL is an open-source library with a family of state-of-the-art online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification.

Changes:

In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows:

• Add a template and guide for adding new algorithms;

• Improve parameter settings and make documentation clear;

• Improve documentation on data formats and key functions;

• Amend the "OGD" function to use different loss types;

• Fixed some name inconsistency and other minor bugs.


Logo r-cran-CORElearn 1.51.2

by r-cran-robot - August 8, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 53458 views, 12290 downloads, 0 subscriptions

About: Classification, Regression and Feature Evaluation

Changes:

Fetched by r-cran-robot on 2018-01-01 00:00:07.164852


Logo FEAST 2.0.0

by apocock - January 8, 2017, 00:49:19 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 72930 views, 12262 downloads, 0 subscriptions

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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:

Major refactoring of FEAST to improve speed and portability.

  • FEAST now clones the input data if it's floating point and discretises it to unsigned ints once in a single pass. This improves the speed by about 30%.
  • FEAST now has unsigned int entry points which avoid this discretisation and are much faster if the data is already categorical.
  • Added weighted feature selection algorithms to FEAST which can be used for cost-sensitive feature selection.
  • Added a Java API using JNI.
  • FEAST now returns the internal score for each feature according to the criterion. Available in all three APIs.
  • Rearranged the repository to make it easier to work with. Header files are now in `include`, source in `src`, the MATLAB API is in `matlab/` and the Java API is in `java/`.
  • FEAST now compiles cleanly using `-std=c89 -Wall -Werror`.

Logo peewit 0.10

by lorenz - May 7, 2014, 16:04:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 61825 views, 12214 downloads, 0 subscriptions

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 Libra 1.1.2d

by lowd - February 4, 2016, 08:51:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 56852 views, 12055 downloads, 0 subscriptions

About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, arithmetic circuits, and mixtures of trees.

Changes:

Version 1.1.2d (12/29/2015):

  • Minor fixes to scripts
  • Published in JMLR ML-OSS!

Logo ELKI 0.7.1

by erich - March 14, 2016, 13:44:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 63515 views, 12005 downloads, 0 subscriptions

About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.

Changes:

Additions and improvements from ELKI 0.7.0 to 0.7.1:

Algorithm additions:

  • GriDBSCAN: DBSCAN using grid partitioning (Minkowski distances only)

  • Compare-Means and Sort-Means k-means variations (much faster than traditional k-means)

  • Visualization of dendrograms.

Important bug fixes:

  • Classes with no package ("default package") would cause errors.

  • The fast power function implementation was sometimes returning incorrect results.

  • Random sampling was sometimes not sampling from the full data set.

UI improvements:

  • The file input source will now automatically choose the Arff parser for .arff files.

  • MiniGUI now allows choosing other applications.

  • MiniGUI now displays the command line in a separate field.

  • MiniGUI displays an error message, if an incorrect classpath or JAyatana (on Ubuntu) is detected.

  • Export to png now works, we added a work-around for an open Batik bug.

Smaller changes:

  • Many smaller bug fixes.

  • C-Index for cluster evaluation now can process larger data sets.

  • OPTICS output of undefined reachability fixed.

  • External distance matrixes are easier to use and perform additional checks.

  • Precomputed distance matrixes can answer range and kNN queries.

  • Voronoi visualization can be switched in the menu now.

  • Improved backwards command line compatibility with additional aliases.

  • Added generated @since annotations in JavaDoc.

  • Many new unit tests, renamed to the Java conventions.

  • Low-level reading of service files, to have faster startup.


Logo JMLR SSA Toolbox 1.3

by paulbuenau - January 24, 2012, 15:51:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 45317 views, 11959 downloads, 0 subscriptions

About: The SSA Toolbox is an efficient, platform-independent, standalone implementation of the Stationary Subspace Analysis algorithm with a friendly graphical user interface and a bridge to Matlab. Stationary Subspace Analysis (SSA) is a general purpose algorithm for the explorative analysis of non-stationary data, i.e. data whose statistical properties change over time. SSA helps to detect, investigate and visualize temporal changes in complex high-dimensional data sets.

Changes:
  • Various bugfixes.

Logo r-cran-pamr 1.54

by r-cran-robot - April 1, 2013, 00:00:06 CET [ Project Homepage BibTeX Download ] 56520 views, 11911 downloads, 0 subscriptions

About: Pam

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.709586


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