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Logo Stabilized Infinite Kernel Learning 1.0.0

by ghiasi - April 10, 2010, 08:45:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4028 views, 697 downloads, 1 subscription

About: This software is designed for learning translation invariant kernels for classification with support vector machines.

Changes:

Initial Announcement on mloss.org.


Logo StirlingNumbers 1.0

by stefanwebb - December 9, 2013, 03:26:56 CET [ Project Homepage BibTeX Download ] 659 views, 182 downloads, 1 subscription

About: A library for calculating and accessing generalized Stirling numbers of the second kind, which are used for inference in Poisson-Dirichlet processes.

Changes:

Initial Announcement on mloss.org.


Logo stroll 0.1

by ppletscher - April 1, 2009, 14:32:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3849 views, 856 downloads, 1 subscription

About: stroll (STRuctured Output Learning Library) is a library for Structured Output Learning.

Changes:

Initial Announcement on mloss.org.


Logo Supervised Latent Semantic Indexing 1.0.0

by openpr_nlpr - December 2, 2011, 05:20:50 CET [ Project Homepage BibTeX Download ] 997 views, 287 downloads, 1 subscription

About: Supervised Latent Semantic Indexing(SLSI) is an supervised feature transformation method. The algorithms in this package are based on the iterative algorithm of Latent Semantic Indexing.

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 ] 11089 views, 3293 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.


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

Changes:

JMLR MLOSS version.


Logo SVM and Kernel Methods Toolbox 0.5

by arakotom - June 10, 2008, 21:29:39 CET [ Project Homepage BibTeX Download ] 9072 views, 2130 downloads, 2 subscriptions

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About: SVM Toolbox fully written in Matlab (even the QP solver). Features : SVM, MultiClassSVM, One-Class, SV Regression, AUC-SVM and Rankboost, 1-norm SVM, Regularization Networks, Kernel Basis Pursuit [...]

Changes:

Initial Announcement on mloss.org.


Logo SVM with uncertain labels 0.2

by rflamary - July 17, 2012, 11:06:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4348 views, 902 downloads, 2 subscriptions

About: Matlab code for learning probabilistic SVM in the presence of uncertain labels.

Changes:

Added missing dataset function (thanks to Hao Wu)


Logo SVMlin 1.0

by vikas - November 27, 2007, 08:04:48 CET [ Project Homepage BibTeX Download ] 4974 views, 1058 downloads, 1 subscription

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About: SVMlin: Fast Linear SVMs for Supervised and Semi-supervised Learning

Changes:

Initial Announcement on mloss.org.


Logo svmPRAT 1.0

by rangwala - December 28, 2009, 00:27:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3573 views, 884 downloads, 1 subscription

About: BACKGROUND:Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. RESULTS:We present a general purpose protein residue annotation toolkit (svmPRAT) to allow biologists to formulate residue-wise prediction problems. svmPRAT formulates the annotation problem as a classification or regression problem using support vector machines. One of the key features of svmPRAT is its ease of use in incorporating any user-provided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible window-based encoding scheme that accurately captures signals and pattern for training eective predictive models. CONCLUSIONS:In this work we evaluate svmPRAT on several classification and regression problems including disorder prediction, residue-wise contact order estimation, DNA-binding site prediction, and local structure alphabet prediction. svmPRAT has also been used for the development of state-of-the-art transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easy-to-use tool for a wide variety of annotation problems. Availability: http://www.cs.gmu.edu/~mlbio/svmprat/

Changes:

Initial Announcement on mloss.org.


Showing Items 481-490 of 536 on page 49 of 54: First Previous 44 45 46 47 48 49 50 51 52 53 54 Next