Projects supporting the matlab data format.
Showing Items 61-65 of 65 on page 4 of 4: Previous 1 2 3 4

Logo MATLAB spectral clustering package 1.1

by wenyenc - February 4, 2010, 01:54:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15929 views, 2977 downloads, 1 subscription

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About: A MATLAB spectral clustering package to deal with large data sets. Our tool can handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. Spectral clustering algorithm has been [...]

Changes:
  • Add bib
  • Add code of nystrom without orthogonalization
  • Add accuracy quality measure
  • Add more running scripts

Logo OXlearn 1.0

by gwestermann - January 11, 2010, 11:48:26 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3895 views, 800 downloads, 1 subscription

About: OXlearn is a free neural network simulation software that enables you to build, train, test and analyse connectionist neural network models. Because OXlearn is implemented as a Matlab toolbox you can run it on all operation systems (Windows, Linux, MAC, etc.), and there is a compiled version for XP.

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 ] 15915 views, 7221 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.


About: Matlab code for performing variational inference in the Indian Buffet Process with a linear-Gaussian likelihood model.

Changes:

Initial Announcement on mloss.org.


Showing Items 61-65 of 65 on page 4 of 4: Previous 1 2 3 4