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Logo WEKA 3.9.0

by mhall - April 15, 2016, 06:35:30 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 56729 views, 8467 downloads, 5 subscriptions

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About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

Changes:

In core weka:

  • JAMA-based linear algebra routines replaced with MTJ. Faster operation with the option to use native libraries for even more speed
  • General efficiency improvements in core, filters and some classifiers
  • GaussianProcesses now handles instance weights
  • New Knowledge Flow implementation. Engine completely rewritten from scratch with a simplified API
  • New Workbench GUI
  • GUI package manager now has a search facility
  • FixedDictionaryStringToWordVector filter allows the use of an external dictionary for vectorization. DictionarySaver converter can be used to create a dictionary file

In packages:

  • Packages that were using JAMA are now using MTJ
  • New netlibNativeOSX, netlibNativeWindows and netlibNativeLinux packages providing native reference implementations (and system-optimized implementation in the case of OSX) of BLAS, LAPACK and ARPACK linear algebra
  • New elasticNet package, courtesy of Nikhil Kinshore
  • New niftiLoader package for loading a directory with MIR data in NIfTI format into Weka
  • New percentageErrorMetrics package - provides plugin evaluation metrics for root mean square percentage error and mean absolute percentage error
  • New iterativeAbsoluteErrorRegression package - provides a meta learner that fits a regression model to minimize absolute error
  • New largeScaleKernelLearning package - contains filters for large-scale kernel-based learning
  • discriminantAnalysis package now contains an implementation for LDA and QDA
  • New Knowledge Flow component implementations in various packages
  • newKnowledgeFlowStepExamples package - contains code examples for new Knowledge Flow API discussion in the Weka Manual
  • RPlugin updated to latest version of MLR
  • scatterPlot3D and associationRulesVisualizer packages updated with latest Java 3D libraries
  • Support for pluggable activation functions in the multiLayerPerceptrons package

About: SVDFeature is a toolkit for developing generic collaborative filtering algorithms by defining features.

Changes:

JMLR MLOSS version.


Logo JMLR libDAI 0.3.2

by jorism - July 17, 2015, 15:59:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 48126 views, 8943 downloads, 4 subscriptions

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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.


Logo JMLR Darwin 1.9

by sgould - September 8, 2015, 06:50:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 47776 views, 10004 downloads, 4 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.

Changes:

Version 1.9:

  • Replaced drwnInPaint class with drwnImageInPainter class and added inPaint application
  • Added function to read CIFAR-10 and CIFAR-100 style datasets (see http://www.cs.utoronto.ca/~kriz/cifar.html)
  • Added drwnMaskedPatchMatch, drwnBasicPatchMatch, drwnSelfPatchMatch and basicPatchMatch application
  • drwnPatchMatchGraph now allows multiple matches to the same image
  • Upgraded wxWidgets to 3.0.2 (problems on Mac OS X)
  • Switched Mac OS X compilation to libc++ instead of libstdc++
  • Added Python scripts for running experiments and regression tests
  • Refactored drwnGrabCutInstance class to support both GMM and colour histogram model
  • Added cacheSortIndex to drwnDecisionTree for trading-off speed versus memory usage
  • Added mexLoadPatchMatchGraph for loading drwnPatchMatchGraph objects into Matlab
  • Improved documentation, other bug fixes and performance improvements

Logo NaN toolbox 3.0.1

by schloegl - March 3, 2016, 20:35:02 CET [ Project Homepage BibTeX Download ] 44773 views, 9216 downloads, 3 subscriptions

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes:

Changes in v.3.0.1 - fix packaging for octave

Changes in v.2.8.5 - bug fix: trimmean - compiler support for gcc-5 and clang - fix typos

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG


Logo r-cran-mboost 2.2-2

by r-cran-robot - February 8, 2013, 00:00:00 CET [ Project Homepage BibTeX Download ] 38158 views, 6964 downloads, 1 subscription

About: Model-Based Boosting

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.324985


Logo MPIKmeans 1.5

by pgehler - January 16, 2009, 15:48:47 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 37220 views, 5638 downloads, 1 subscription

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About: A K-means clustering implementation for command-line, Python, Matlab and C. This algorithm yields the very same solution as standard Kmeans, even after each iteration. However it uses some triangle [...]

Changes:

Initial Announcement on mloss.org.


Logo JMLR Sally 1.0.0

by konrad - March 26, 2015, 17:01:35 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 36875 views, 7161 downloads, 3 subscriptions

About: A Tool for Embedding Strings in Vector Spaces

Changes:

Support for explicit selection of granularity added. Several minor bug fixes. We have reached 1.0


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 36028 views, 6070 downloads, 1 subscription

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 PyMVPA Multivariate Pattern Analysis in Python 2.0.0

by yarikoptic - December 22, 2011, 01:36:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 35697 views, 6338 downloads, 1 subscription

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About: Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, [...]

Changes:
  • 2.0.0 (Mon, Dec 19 2011)

This release aggregates all the changes occurred between official releases in 0.4 series and various snapshot releases (in 0.5 and 0.6 series). To get better overview of high level changes see :ref:release notes for 0.5 <chap_release_notes_0.5> and :ref:0.6 <chap_release_notes_0.6> as well as summaries of release candidates below

  • Fixes (23 BF commits)

    • significance level in the right tail was fixed to include the value tested -- otherwise resulted in optimistic bias (or absurdly high significance in improbable case if all estimates having the same value)
    • compatible with the upcoming IPython 0.12 and renamed sklearn (Fixes #57)
    • do not double-train slave classifiers while assessing sensitivities (Fixes #53)
  • Enhancements (30 ENH + 3 NF commits)

    • resolving voting ties in kNN based on mean distance, and randomly in SMLR
    • :class:kNN's ca.estimates now contains dictionaries with votes for each class
    • consistent zscoring in :class:Hyperalignment
  • 2.0.0~rc5 (Wed, Oct 19 2011)

  • Major: to allow easy co-existence of stable PyMVPA 0.4.x, 0.6 development mvpa module was renamed into mod:mvpa2.

  • Fixes

    • compatible with the new Shogun 1.x series
    • compatible with the new h5py 2.x series
    • mvpa-prep-fmri -- various compatibility fixes and smoke testing
    • deepcopying :class:SummaryStatistics during add
  • Enhancements

    • tutorial uses :mod:mvpa2.tutorial_suite now
    • better suppression of R warnings when needed
    • internal attributes of many classes were exposed as properties
    • more unification of __repr__ for many classes
  • 0.6.0~rc4 (Wed, Jun 14 2011)

  • Fixes

    • Finished transition to :mod:nibabel conventions in plot_lightbox
    • Addressed :mod:matplotlib.hist API change
    • Various adjustments in the tests batteries (:mod:nibabel 1.1.0 compatibility, etc)
  • New functionality

    • Explicit new argument flatten to from_wizard -- default behavior changed if mapper was provided as well
  • Enhancements

    • Elaborated __str__ and __repr__ for some Classifiers and Measures
  • 0.6.0~rc3 (Thu, Apr 12 2011)

  • Fixes

    • Bugfixes regarding the interaction of FlattenMapper and BoxcarMapper that affected event-related analyses.
    • Splitter now handles attribute value None for splitting properly.
    • GNBSearchlight handling of
      roi_ids.
    • More robust detection of mod:scikits.learn and :mod:nipy externals.
  • New functionality

    • Added a Repeater node to yield a dataset multiple times and
      Sifter node to exclude some datasets. Consequently, the "nosplitting" mode of Splitter got removed at the same time.
    • :file:tools/niils -- little tool to list details (dimensionality, scaling, etc) of the files in nibabel-supported formats.
  • Enhancements

    • Numerous documentation fixes.
    • Various improvements and increased flexibility of null distribution estimation of Measures.
    • All attribute are now reported in sorted order when printing a dataset.
    • fmri_dataset now also stores the input image type.
    • Crossvalidation can now take a custom Splitter instance. Moreover, the default splitter of CrossValidation is more robust in terms of number and type of created splits for common usage patterns (i.e. together with partitioners).
    • CrossValidation takes any custom Node as errorfx argument.
    • ConfusionMatrix can now be used as an errorfx in Crossvalidation.
    • LOE(ACC): Linear Order Effect in ACC was added to
      ConfusionMatrix to detect trends in performances across splits.
    • A Node s postproc is now accessible as a property.
    • RepeatedMeasure has a new 'concat_as' argument that allows results to be concatenated along the feature axis. The default behavior, stacking as multiple samples, is unchanged.
    • Searchlight now has the ability to mark the center/seed of an ROI in with a feature attribute in the generated datasets.
    • debug takes args parameter for delayed string comprehensions. It should reduce run-time impact of debug() calls in regular, non -O mode of Python operation.
    • String summaries and representations (provided by __str__ and __repr__) were made more exhaustive and more coherent. Additional properties to access initial constructor arguments were added to variety of classes.
  • Internal changes

    • New debug target STDOUT to allow attaching metrics (e.g. traceback, timestamps) to regular output printed to stdout

    • New set of decorators to help with unittests

    • @nodebug to disable specific debug targets for the duration of the test.

    • @reseed_rng to guarantee consistent random data given initial seeding.

    • @with_tempfile to provide a tempfile name which would get removed upon completion (test success or failure)

    • Dropping daily testing of maint/0.5 branch -- RIP.

    • Collection s were provided with adequate (deep|)copy. And Dataset was refactored to use Collection s copy method.

    • update-* Makefile rules automatically should fast-forward corresponding website-updates branch

    • MVPA_TESTS_VERBOSITY controls also :mod:numpy warnings now.

    • Dataset.__array__ provides original array instead of copy (unless dtype is provided)

Also adapts changes from 0.4.6 and 0.4.7 (see corresponding changelogs).

  • 0.6.0~rc2 (Thu, Mar 3 2011)

  • Various fixes in the mvpa.atlas module.

  • 0.6.0~rc1 (Thu, Feb 24 2011)

  • Many, many, many

  • For an overview of the most drastic changes :ref:see constantly evolving release notes for 0.6 <chap_release_notes_0.6>

  • 0.5.0 (sometime in March 2010)

This is a special release, because it has never seen the general public. A summary of fundamental changes introduced in this development version can be seen in the :ref:release notes <chap_release_notes_0.5>.

Most notably, this version was to first to come with a comprehensive two-day workshop/tutorial.

  • 0.4.7 (Tue, Mar 07 2011) (Total: 12 commits)

A bugfix release

  • Fixed

    • Addressed the issue with input NIfTI files having scl_ fields set: it could result in incorrect analyses and map2nifti-produced NIfTI files. Now input files account for scaling/offset if scl_ fields direct to do so. Moreover upon map2nifti, those fields get reset.
    • :file:doc/examples/searchlight_minimal.py - best error is the minimal one
  • Enhancements

    • :class:~mvpa.clfs.gnb.GNB can now tolerate training datasets with a single label
    • :class:~mvpa.clfs.meta.TreeClassifier can have trailing nodes with no classifier assigned
  • 0.4.6 (Tue, Feb 01 2011) (Total: 20 commits)

A bugfix release

  • Fixed (few BF commits):

    • Compatibility with numpy 1.5.1 (histogram) and scipy 0.8.0 (workaround for a regression in legendre)
    • Compatibility with libsvm 3.0
    • :class:~mvpa.clfs.plr.PLR robustification
  • Enhancements

    • Enforce suppression of numpy warnings while running unittests. Also setting verbosity >= 3 enables all warnings (Python, NumPy, and PyMVPA)
    • :file:doc/examples/nested_cv.py example (adopted from 0.5)
    • Introduced base class :class:~mvpa.clfs.base.LearnerError for classifiers' exceptions (adopted from 0.5)
    • Adjusted example data to live upto nibabel's warranty of NIfTI standard-compliance
    • More robust operation of MC iterations -- skip iterations where classifier experienced difficulties and raise an exception (e.g. due to degenerate data)

Showing Items 11-20 of 619 on page 2 of 62: Previous 1 2 3 4 5 6 7 Next Last