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About: A comprehensive data mining environment, with a variety of machine learning components. Changes:Modifications following feedback from Knime main Author.
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About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini. Changes:
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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:http://sourceforge.net/projects/weka/files/weka-3-7/3.7.9/README-3-7-9.txt/view
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About: LIBOL is an open-source library that consists of a family of state-of-the-art online learning algorithms for machine learning and data mining research. Changes:Initial Announcement on mloss.org.
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About: Automatic Analysis of Malware Behavior using Machine Learning Changes:The tool's persistent state is stored in the local state directory (i.e. /var) for better maintenance. Several minor bugs have been fixed.
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About: C++ software for statistical classification, probability estimation and interpolation/non-linear regression using variable bandwidth kernel estimation. Changes:New in Version 0.9.6:
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About: This package contains a python and a matlab implementation of the most widely used algorithms for multi-armed bandit problems. The purpose of this package is to provide simple environments for comparison and numerical evaluation of policies. Changes:Initial Announcement on mloss.org.
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About: Quantile Regression Forests Changes:Fetched by r-cran-robot on 2013-04-01 00:00:07.576421
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About: Relaxed Lasso Changes:Fetched by r-cran-robot on 2013-04-01 00:00:07.978325
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About: Software to perform isoline retrieval, retrieve isolines of an atmospheric parameter from a nadir-looking satellite. Changes:Added screenshot, keywords
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About: Multivariate partitioning Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.387032
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About: Matlab SVM toolbox for learning large margin filters in signal or images. Changes:Initial Announcement on mloss.org.
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About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.
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About: MLPlot is a lightweight plotting library written in Java. Changes:Initial Announcement on mloss.org.
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About: Regression Trees with Random Effects for Longitudinal (Panel) Data Changes:Fetched by r-cran-robot on 2013-04-01 00:00:08.040424
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About: The K-tree is a scalable approach to clustering inspired by the B+-tree and k-means algorithms. Changes:Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.
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About: The open source Error-Correcting Output Codes (ECOC) library contains both state-of-the-art coding and decoding designs, as well as the option to include your own coding, decoding, and base classifier. Changes:Initial Announcement on mloss.org.
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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.
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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.
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