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Logo JMLR Mulan 1.4.0

by lefman - August 1, 2012, 09:49:21 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12353 views, 5450 downloads, 1 subscription

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

  • BinaryRelevance.java: improved data handling that avoids copying the entire input space, leading to important speedups in case of large datasets and very large number of labels.
  • RAkEL.java: updated technical information, added a check for the case where the number of labels is less or equal than the size of the subset.
  • MultiLabelKNN.java: now checks whether the number of instances is less than the number of requested nearest neighbors.
  • Addition of AdaBoostMH.java, an explicit implementation of AdaBoost.MH as combination of AdaBoostM1 and IncludeLabelsClassifier.
  • Addition of MLPTO.java, the Multi Label Probabilistic Threshold Optimizer (MLTPTO) thresholding technique.
  • Addition of ApproximateExampleBasedFMeasureOptimizer.java, an approximate method for the maximization of example-based F-measure.

Measures/Evaluation

  • Addition of Specificity measure (example-based, micro/macro label-based)
  • Addition of Mean Average Interpolated Precision (MAiP), Geometric Mean Average Precision (GMAP), Geometric Mean Average Interpolated Precision (GMAiP).
  • New methods for stratified multi-label evaluation.
  • Added support for outputting per label results for all measures that implement the MacroAverageMeasure interface.
  • Simplifying the "strictness" issue of information retrieval measures, by adopting specific assumptions (outlined in the new class InformationRetrievalMeasures.java) to handle special cases, instead of the less clear and useful solution of outputting NaN and the less realistic solution or ignoring special cases.

Bug fixes

  • Bug fix in LabelsBuilder.java.
  • Bug fix in Ranker.java.
  • Bug-fix in ThresholdPrediction.java.
  • Fix for bug occurring when loading the XSD for mulan data outside the command-line environment (e.g. web applications).
  • Javadoc comment updates.

API changes

  • Upgrade to Java 1.6
  • Upgrade to JUnit 4.10
  • Upgrade to Weka 3.7.6.

Miscellaneous

  • Meaningful messages are now shown when a DataLoadException is thrown.
  • PT6(PT6Transformation.java): renamed to IncludeLabelsTransformation.java.
  • MultiLabelInstances now support serialization, as needed by the improved binary relevance transformation.
  • BinaryRelevanceAttributeEvaluator.java: updated according to latest BR improvements.

Logo JMLR Model Monitor 1.0

by traeder - August 17, 2009, 11:05:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11840 views, 1646 downloads, 0 comments, 1 subscription

About: Model Monitor is a Java toolkit for the systematic evaluation of classifiers under changes in distribution. It provides methods for detecting distribution shifts in data, comparing the performance [...]

Changes:

Improved AUROC calculation. Several minor bug fixes.


Logo r-cran-e1071 1.6-3

by r-cran-robot - February 13, 2014, 00:00:00 CET [ Project Homepage BibTeX Download ] 11706 views, 2405 downloads, 1 subscription

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About: Misc Functions of the Department of Statistics (e1071), TU Wien

Changes:

Fetched by r-cran-robot on 2014-04-01 00:00:04.937452


Logo JMLR JNCC2 1.11

by gcorani - January 1, 2009, 03:22:47 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11603 views, 1388 downloads, 0 comments, 1 subscription

About: JNCC2 is the open-source implementation of the Naive Credal Classifier2 (NCC2), i.e., an extension of Naive Bayes towards imprecise probabilities, designed to deliver robust classifications even on [...]

Changes:

Initial Announcement on mloss.org.


Logo FEAST 1.00

by apocock - February 13, 2012, 19:00:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11515 views, 2805 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.

Changes:

Initial Announcement on mloss.org.


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 DAL 1.1

by ryota - February 18, 2014, 19:07:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11364 views, 1988 downloads, 1 subscription

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/low-rank learning/reconstruction based on the dual augmented Lagrangian method.

Changes:
  • Supports weighted lasso (dalsqal1.m, dallral1.m)
  • Supports weighted squared loss (dalwl1.m)
  • Bug fixes (group lasso and elastic-net-regularized logistic regression)

Logo Theano 0.6

by jaberg - December 3, 2013, 20:32:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11300 views, 2135 downloads, 1 subscription

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 0.6 (December 3th, 2013)

Highlight:

* Last release with support for Python 2.4 and 2.5.
* We will try to release more frequently.
* Fix crash/installation problems.
* Use less memory for conv3d2d.

0.6rc4 skipped for a technical reason.

Highlights (since 0.6rc3):

* Python 3.3 compatibility with buildbot test for it.
* Full advanced indexing support.
* Better Windows 64 bit support.
* New profiler.
* Better error messages that help debugging.
* Better support for newer NumPy versions (remove useless warning/crash).
* Faster optimization/compilation for big graph.
* Move in Theano the Conv3d2d implementation.
* Better SymPy/Theano bridge: Make an Theano op from SymPy expression and use SymPy c code generator.
* Bug fixes.

Too much changes in 0.6rc1, 0.6rc2 and 0.6rc3 to list here. See https://github.com/Theano/Theano/blob/master/NEWS.txt for details.


Logo r-cran-arules 1.1-2

by r-cran-robot - February 21, 2014, 00:00:00 CET [ Project Homepage BibTeX Download ] 11268 views, 2304 downloads, 2 subscriptions

About: Mining Association Rules and Frequent Itemsets

Changes:

Fetched by r-cran-robot on 2014-04-01 00:00:04.046616


Logo Maja Machine Learning Framework 1.0

by jhm - September 13, 2011, 15:13:56 CET [ Project Homepage BibTeX Download ] 10960 views, 2231 downloads, 1 subscription

About: The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents.

Changes:
  • Experiments can now be invoked from the command line
  • Experiments can now be "scripted"
  • MMLF Experimenter contains now basic module for statistical hypothesis testing
  • MMLF Explorer can now visualize the model that has been learned by an agent

Showing Items 61-70 of 519 on page 7 of 52: First Previous 2 3 4 5 6 7 8 9 10 11 12 Next Last