About: FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search. Changes:See project page for changes.

About: GritBot is an data cleaning and outlier/anomaly detection program. Changes:Initial Announcement on mloss.org.

About: Cubist is the regression counterpart to the C5.0 decision tree tool. Changes:Initial Announcement on mloss.org.

About: C5.0 is the successor of the C4.5 decision tree algorithm/tool. In particular, it is faster and more memoryefficient. Changes:Initial Announcement on mloss.org.

About: A Sortware for All Pairs Similarity Search Changes:Initial Announcement on mloss.org.

About: OpenViBE is an opensource platform that enables to design, test and use BrainComputer Interfaces (BCI). Broadly speaking, OpenViBE can be used in many realtime Neuroscience applications [...] Changes:New release 0.8.0.

About: jblas is a fast linear algebra library for Java. jblas is based on BLAS and LAPACK, the defacto industry standard for matrix computations, and uses stateoftheart implementations like ATLAS for all its computational routines, making jBLAS very fast. Changes:Changes from 1.0:

About: redsvd is a library for solving several matrix decomposition (SVD, PCA, eigen value decomposition) redsvd can handle very large matrix efficiently, and optimized for a truncated SVD of sparse matrices. For example, redsvd can compute a truncated SVD with top 20 singular values for a 100K x 100K matrix with 10M nonzero entries in about two second. Changes:Initial Announcement on mloss.org.

About: The library implements Optimized Cutting Plane Algorithm (OCAS) for efficient training of linear SVM classifiers from largescale data. Changes:Implemented COFFIN framework which allows efficient training of invariant image classifiers via virtual examples.

About: HSSVM is a software for solving multiclass problem using Hypersphere Support Vector Machines model, implemented by Java. Changes:

About: ELF provides many well implemented supervised learners for classification and regression tasks with an opportunity of ensemble learning. Changes:Initial Announcement on mloss.org.

About: Accurate splice site predictor for a variety of genomes. Changes:Asp now supports three formats: g fname for gff format s fname for spf format b dir for a binary format compatible with mGene. And a new switch t which switches on a sigmoidbased transformation of the svm scores to get scores between 0 and 1.

About: This software is an implementation of Hidden Markov Support Vector Machines (HMSVMs). Changes:Initial Announcement on mloss.org.

About: This software is designed for learning translation invariant kernels for classification with support vector machines. Changes:Initial Announcement on mloss.org.

About: GIDOC (Gimpbased Interactive transcription of old text DOCuments) is a computerassisted transcription prototype for handwritten text in old documents. It is a first attempt to provide integrated support for interactivepredictive page layout analysis, text line detection and handwritten text transcription. GIDOC is built on top of the wellknown GNU Image Manipulation Program (GIMP), and uses standard techniques and tools for handwritten text preprocessing and feature extraction, HMMbased image modelling, and language modelling. Changes:Updated version for mloss 2010

About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others. Changes:

About: LIBSVM is an integrated software for support vector classification, (CSVC, nuSVC ), regression (epsilonSVR, nuSVR) and distribution estimation (oneclass SVM). It supports multiclass [...] Changes:Initial Announcement on mloss.org.

About: GPUML is a library that provides a C/C++ and MATLAB interface for speeding up the computation of the weighted kernel summation and kernel matrix construction on GPU. These computations occur commonly in several machine learning algorithms like kernel density estimation, kernel regression, kernel PCA, etc. Changes:Initial Announcement on mloss.org.

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:

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 sequencederived 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 residuewise 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 userprovided 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 windowbased 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, residuewise contact order estimation, DNAbinding site prediction, and local structure alphabet prediction. svmPRAT has also been used for the development of stateoftheart transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easytouse tool for a wide variety of annotation problems. Availability: http://www.cs.gmu.edu/~mlbio/svmprat/ Changes:Initial Announcement on mloss.org.
