Projects that are tagged with regression.
Showing Items 1-20 of 26 on page 1 of 2: 1 2 Next

Logo Orange 2.0 beta

by janez - August 23, 2010, 09:57:35 CET [ Project Homepage BibTeX Download ] 3068 views, 813 downloads, 0 subscriptions

About: Orange is a component-based machine learning and data mining software. It includes a friendly yet powerful and flexible graphical user interface for visual programming. For more advanced use(r)s, [...]

Changes:

Update for v2.0


Logo MLPY Machine Learning Py 2.2.1

by albanese - August 17, 2010, 14:45:50 CET [ Project Homepage BibTeX Download ] 15663 views, 3358 downloads, 2 subscriptions

Rating Whole StarEmpty StarEmpty StarEmpty StarEmpty Star
(based on 1 vote)

About: Machine Learning PYthon (mlpy) is a high-performance Python package for predictive modeling.

Changes:

New features:

  • Elastic Net
  • FSSun speeded up
  • doctests added (mlpy-tests)
  • Documentation improved

Several bugs fixed


Logo Surrogate Modeling Toolbox 7.0.2

by dgorissen - August 10, 2010, 10:58:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 843 views, 183 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 WEKA 3.7.2

by mhall - August 2, 2010, 11:17:22 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9591 views, 1829 downloads, 2 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarEmpty Star
(based on 6 votes)

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:

Initial Announcement on mloss.org.


Logo PSVM 1.31

by mhex - July 29, 2010, 10:02:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 293 views, 51 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 2 votes)

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

Changes:

Initial Announcement on mloss.org.


Logo dlib ml 17.30

by davis685 - July 29, 2010, 03:08:47 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13811 views, 2947 downloads, 1 subscription

About: A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

Changes:

Minor bug fixes


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 ] 281 views, 53 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
(based on 1 vote)

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 GPML Gaussian Processes for Machine Learning Toolbox 3.0

by hn - July 23, 2010, 12:13:58 CET [ Project Homepage BibTeX Download ] 446 views, 63 downloads, 1 subscription

About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models.

Changes:

Initial Announcement on mloss.org.


Logo LIBSVM 2.9

by cjlin - February 27, 2010, 01:09:23 CET [ Project Homepage BibTeX Download ] 5113 views, 1112 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole StarEmpty Star
(based on 5 votes)

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.


Logo PyMVPA Multivariate Pattern Analysis in Python 0.4.4

by yarikoptic - February 7, 2010, 16:48:00 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 13035 views, 2638 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarWhole StarEmpty Star
(based on 2 votes)

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:

0.4.4 (Mon, Feb 2 2010) (Total: 144 commits)

Primarily a bugfix release, probably the last in 0.4 series since development for 0.5 release is leaping forward.

  • New functionality (19 NF commits):

o GNB implements Gaussian Naïve Bayes Classifier.

o read_fsl_design() to read FSL FEAT design.fsf files (Contributed by Russell A. Poldrack).

o SequenceStats to provide basic statistics on labels sequence (counter-balancing, autocorrelation).

o New exceptions DegenerateInputError and FailedToTrainError to be thrown by classifiers primarily during training/testing.

o Debug target STATMC to report on progress of Monte-Carlo sampling (during permutation testing).

  • Refactored (15 RF commits):

o To get users prepared to 0.5 release, internally and in some examples/documentation, access to states and parameters is done via corresponding collections, not from the top level object (e.g. clf.states.predictions instead of soon-to-be-deprecated clf.predictions). That should lead also to improved performance.

o Adopted copy.py from python2.6 (support Ellipsis as well). ed (38 BF commits):

o GLM output does not depend on the enabled states any more.

o Variety of docstrings fixed and/or improved.

o Do not derive NaN scaling for SVM’s C whenever data is degenerate (lead to never finishing SVM training).

o sg : + KRR is optional now – avoids crashing if KRR is not available.

  • tolerance to absent set_precompute_matrix in svmlight in recent shogun versions.

  • support for recent (present in 0.9.1) API change in exposing debug levels.

o Python 2.4 compatibility issues: kNN and IFS


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

Changes:

Initial Announcement on mloss.org.


Logo LWPR 1.2.3

by sklanke - November 12, 2009, 11:57:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 12527 views, 1495 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 1.2.3

  • Corrected bugs in the Python module (missing Py_DECREF in functions returning multiple numpy arrays that led to a memory leak, as well as bad preprocessor directive "USE_EXPAT" instead of "HAVE_LIBEXPAT") Thanks to Benjamin Dittes

  • Corrected bad preprocessor directive (see above) in C++ wrapper lwpr.hh. Also added casts between signed and unsigned integers in lwpr.hh and cross.cc Thanks to Peter Pastor, Robert Nickl and Adrian Haith


Logo Elefant 0.4

by kishorg - October 17, 2009, 08:48:19 CET [ Project Homepage BibTeX Download ] 7325 views, 4479 downloads, 2 subscriptions

Rating Whole StarWhole Star1/2 StarEmpty StarEmpty Star
(based on 2 votes)

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 ] 1804 views, 346 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 ] 3866 views, 758 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 ] 2269 views, 467 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 ] 8576 views, 1183 downloads, 1 subscription

Rating Whole StarWhole StarWhole Star1/2 StarEmpty Star
(based on 3 votes)

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 ] 3023 views, 642 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.


Logo Experiment Databases for Machine Learning 0.1

by JoaquinVanschoren - October 7, 2008, 18:06:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3118 views, 503 downloads, 1 subscription

About: Experiment Databases for Machine Learning is a large public database of machine learning experiments as well as a framework for producing similar databases for specific goals. It provides a way to [...]

Changes:

Initial Announcement on mloss.org.


Showing Items 1-20 of 26 on page 1 of 2: 1 2 Next