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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 ] 24607 views, 8104 downloads, 2 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 JMLR Java Machine Learning Library 0.1.5

by thomas - August 20, 2009, 23:47:45 CET [ Project Homepage BibTeX Download ] 23855 views, 3241 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 ] 23269 views, 5404 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 gensim 0.8.6

by Radim - December 9, 2012, 13:15:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22831 views, 4948 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 MDP Modular toolkit for Data Processing 3.3

by otizonaizit - October 4, 2012, 15:17:33 CET [ Project Homepage BibTeX Download ] 22829 views, 5696 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 Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 22330 views, 8544 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 scikitlearn 0.17.1

by fabianp - July 28, 2016, 20:05:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22234 views, 8371 downloads, 4 subscriptions

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About: The scikit-learn project is a machine learning library in Python.

Changes:

Update for 0.17.1


Logo JMLR DLLearner 1.0

by Jens - February 13, 2015, 11:39:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22067 views, 4998 downloads, 6 subscriptions

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About: The DL-Learner framework contains several algorithms for supervised concept learning in Description Logics (DLs) and OWL.

Changes:

See http://dl-learner.org/development/changelog/.


Logo JMLR MSVMpack 1.5

by lauerfab - July 3, 2014, 16:02:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22057 views, 6796 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 ] 21958 views, 4928 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


Showing Items 51-60 of 623 on page 6 of 63: Previous 1 2 3 4 5 6 7 8 9 10 11 Next Last