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Showing Items 321-330 of 619 on page 33 of 62: First Previous 28 29 30 31 32 33 34 35 36 37 38 Next Last

Logo r-cran-relaxo 0.1-2

by r-cran-robot - June 1, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 4648 views, 1166 downloads, 1 subscription

About: Relaxed Lasso

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:07.978325


About: The package provides a Lagrangian approach to the posterior regularization of given linear mappings. This is important in two cases, (a) when systems are under-determined and (b) when the external model for calculating the mapping is invariant to properties such as scaling. The software may be applied in cases when the external model does not provide its own regularization strategy. In addition, the package allows to rank attributes according to their distortion potential to a given linear mapping.

Changes:

Version 1.1 (May 23, 2012) memory and time optimizations distderivrel.m now supports assessing the relevance of attribute pairs

Version 1.0 (Nov 9, 2011) * Initial Announcement on mloss.org.


About: This code is developed for incorporating a class of linear priors into the regression model.

Changes:

Initial Announcement on mloss.org.


Logo WebEnsemble 1.0

by jungc005 - May 8, 2012, 22:24:44 CET [ BibTeX Download ] 2180 views, 833 downloads, 1 subscription

About: Use the power of crowdsourcing to create ensembles.

Changes:

Initial Announcement on mloss.org.


Logo Partition Comparison 1.0

by andres - April 21, 2012, 03:26:47 CET [ Project Homepage BibTeX Download ] 2772 views, 773 downloads, 1 subscription

About: Fast C++ implementation of the variation of information (Meila 2003) and Rand index (Rand 1971) with MATLAB mex files

Changes:

Initial Announcement on mloss.org.


Logo Multilinear Principal Component Analysis 1.2 1.2

by openpr_nlpr - April 16, 2012, 09:04:08 CET [ Project Homepage BibTeX Download ] 2934 views, 863 downloads, 1 subscription

About: This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 18-39, January 2008.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-oblique.tree 1.1

by r-cran-robot - April 14, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 3584 views, 693 downloads, 0 subscriptions

About: Oblique Trees for Classification Data

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:06.648184


Logo Oboe A Chinese Syntactic Parser 1.0

by openpr_nlpr - April 9, 2012, 09:08:35 CET [ Project Homepage BibTeX Download ] 3085 views, 693 downloads, 1 subscription

About: Oboe is a software for Chinese syntactic parsing, and it can display syntactic trees in a graphical view with two kinds of representation: phrase tree and dependency tree. So it is very helpful for NLP researchers, especially for researchers focusing on syntax-based methods.

Changes:

Initial Announcement on mloss.org.


Logo TMBP 1.0

by zengjia - April 5, 2012, 06:42:26 CET [ BibTeX BibTeX for corresponding Paper Download ] 6933 views, 3451 downloads, 2 subscriptions

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About: Message passing for topic modeling

Changes:
  1. improve "readme.pdf".
  2. correct some compilation errors.

Logo MLFlex 02-21-2012-00-12

by srp33 - April 3, 2012, 16:44:43 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3402 views, 709 downloads, 1 subscription

About: Motivated by a need to classify high-dimensional, heterogeneous data from the bioinformatics domain, we developed ML-Flex, a machine-learning toolbox that enables users to perform two-class and multi-class classification analyses in a systematic yet flexible manner. ML-Flex was written in Java but is capable of interfacing with third-party packages written in other programming languages. It can handle multiple input-data formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, ML-Flex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.)

Changes:

Initial Announcement on mloss.org.


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