Projects that are tagged with regression.
Showing Items 21-40 of 47 on page 2 of 3: Previous 1 2 3 Next

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

Changes:

Initial Announcement on mloss.org.


Logo MLPY Machine Learning Py 3.5.0

by albanese - March 15, 2012, 09:52:41 CET [ Project Homepage BibTeX Download ] 75430 views, 13755 downloads, 2 subscriptions

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About: mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL.

Changes:

New features:

  • LibSvm(): pred_probability() now returns probability estimates; pred_values() added
  • LibLinear(): pred_values() and pred_probability() added
  • dtw_std: squared Euclidean option added
  • LCS for series composed by real values (lcs_real()) added
  • Documentation

Fix:

  • wavelet submodule: cwt(): it returned only real values in morlet and poul
  • IRelief(): remove np. in learn()
  • fix rfe_kfda and rfe_w2 when p=1

Logo JMLR LWPR 1.2.4

by sklanke - February 6, 2012, 19:55:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 36085 views, 4401 downloads, 1 subscription

About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...]

Changes:

Version 1.2.4

  • Corrected typo in lwpr.c (wrong function name for multi-threaded helper function on Unix systems) Thanks to Jose Luis Rivero

Logo Kernel Machine Library 0.2

by pawelm - December 27, 2011, 17:14:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper ] 6580 views, 281 downloads, 1 subscription

About: The Kernel-Machine Library is a free (released under the LGPL) C++ library to promote the use of and progress of kernel machines.

Changes:

Updated mloss entry (minor fixes).


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 ] 38499 views, 6681 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)

Logo Rudder 0.1

by dmcnelis - December 16, 2011, 22:00:45 CET [ Project Homepage BibTeX Download ] 4887 views, 1502 downloads, 1 subscription

About: An annotated java framework for machine learning, aimed at making it really easy to access analytically functions.

Changes:

Now supports OLS and GLS regression and NaiveBayes classification


Logo RRforest 2002-03-13

by zenog - September 21, 2011, 14:23:44 CET [ Project Homepage BibTeX Download ] 3198 views, 856 downloads, 1 subscription

About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman.

Changes:

Initial Announcement on mloss.org.


Logo Cubist 2.07

by zenog - September 2, 2011, 14:52:17 CET [ Project Homepage BibTeX Download ] 3897 views, 1062 downloads, 1 subscription

About: Cubist is the regression counterpart to the C5.0 decision tree tool.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Surrogate Modeling Toolbox 7.0.2

by dgorissen - September 4, 2010, 07:48:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 17867 views, 4855 downloads, 1 subscription

About: The SUMO Toolbox is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (e.g., simulation code, data set, script, ...) within the accuracy and time constraints set by the user. The toolbox minimizes the number of data points (which it selects automatically) since they are usually expensive.

Changes:

Incremental update, fixing some cosmetic issues, coincides with JMLR publication.


Logo PSVM 1.31

by mhex - July 29, 2010, 10:02:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6413 views, 1638 downloads, 1 subscription

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About: PSVM - Support vector classification, regression and feature extraction for non-square dyadic data, non-Mercer kernels.

Changes:

Initial Announcement on mloss.org.


Logo LSTM for biological sequence analysis 1.0

by mhex - July 28, 2010, 16:32:29 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8716 views, 2087 downloads, 1 subscription

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About: Implementation of LSTM for biological sequence analysis (classification, regression, motif discovery, remote homology detection). Additionally a LSTM as logistic regression with spectrum kernel is included.

Changes:

Spectrum LSTM package included


Logo LIBSVM 2.9

by cjlin - February 27, 2010, 01:09:23 CET [ Project Homepage BibTeX Download ] 13645 views, 2856 downloads, 1 subscription

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About: LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class [...]

Changes:

Initial Announcement on mloss.org.


About: Matlab code for semi-supervised regression and dimensionality reduction using Hessian energy.

Changes:

Initial Announcement on mloss.org.


Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 23068 views, 8703 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 BMRM 2.1

by chteo - May 8, 2009, 08:08:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 7988 views, 1636 downloads, 1 subscription

About: BMRM is an open source, modular and scalable convex solver for many machine learning problems cast in the form of regularized risk minimization problem.

Changes:

Initial Announcement on mloss.org.


Logo Penalized Partial Least Squares Regression 1.03

by nkraemer - May 5, 2009, 19:53:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 11768 views, 1783 downloads, 0 subscriptions

About: This package contains functions to estimate linear and nonlinear regression methods with Penalized Partial Least Squares.

Changes:
  • fixed several bugs
  • drastic speed-up of computation time

About: The package estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, PLS, and Ridge Regression.

Changes:

Initial Announcement on mloss.org.


Logo Graph Learning Package 0.1

by hiroto - May 4, 2009, 17:07:15 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9537 views, 1824 downloads, 0 subscriptions

About: This software is aimed at performing supervised/unsupervised learning on graph data, where each graph is represented as binary indicators of subgraph features.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Nieme 1.0

by francis - April 2, 2009, 10:57:38 CET [ Project Homepage BibTeX Download ] 22328 views, 2988 downloads, 1 subscription

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About: Nieme is a C++ machine learning library for large-scale classification, regression and ranking. It provides a simple interface available in C++, Python and Java and a user interface for visualization.

Changes:

Released Nieme 1.0


Logo BenchMarking Via Weka 0.0.4

by fracpete - December 4, 2008, 01:15:15 CET [ Project Homepage BibTeX Download ] 9135 views, 1651 downloads, 0 comments, 2 subscriptions

About: BenchMarking Via Weka is a client-server architecture that supports interoperability between different machine learning systems. Machine learning systems need to provide mechanisms for processing [...]

Changes:

Initial Announcement on mloss.org.


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