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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 JMLR CARP 3.3

by volmeln - November 7, 2013, 15:48:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 24837 views, 7635 downloads, 1 subscription

About: CARP: The Clustering Algorithms’ Referee Package

Changes:

Generalized overlap error and some bugs have been fixed


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

Changes:

JMLR MLOSS version.


Logo mldata-utils 0.5.0

by sonne - April 8, 2011, 10:02:44 CET [ Project Homepage BibTeX Download ] 34475 views, 7402 downloads, 1 subscription

About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org

Changes:
  • Change task file format, such that data splits can have a variable number items and put into up to 256 categories of training/validation/test/not used/...
  • Various bugfixes.

Logo r-cran-e1071 1.6-8

by r-cran-robot - April 1, 2017, 00:00:04 CET [ Project Homepage BibTeX Download ] 36095 views, 7224 downloads, 3 subscriptions

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About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly

Changes:

Fetched by r-cran-robot on 2017-04-01 00:00:04.887707


Logo JMLR MultiBoost 1.2.02

by busarobi - March 31, 2014, 16:13:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 44328 views, 7129 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 LIBOL 0.3.0

by stevenhoi - December 12, 2013, 15:26:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18295 views, 6914 downloads, 2 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 JMLR SSA Toolbox 1.3

by paulbuenau - January 24, 2012, 15:51:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 22684 views, 6905 downloads, 1 subscription

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 JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20240 views, 6890 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Changes:

New version November 2013


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 ] 39828 views, 6874 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 31-40 of 638 on page 4 of 64: Previous 1 2 3 4 5 6 7 8 9 Next Last