About: Divvy is a Mac OS X application for performing dimensionality reduction, clustering, and visualization.

Initial Announcement on mloss.org.

About: Python Machine Learning Toolkit

Added LASSO (using coordinate descent optimization). Made SVM classification (learning and applying) much faster: 2.5x speedup on yeast UCI dataset.

About: Breiman and Cutler's random forests for classification and regression

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About: ReFr is a software architecture for specifying, training and using reranking models.

Initial Announcement on mloss.org.

About: The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and data-dependent [...]

Minor changes: fix bug on set_alphas_b0 function (thanks to Ferdinand Kaiser - ferdinand.kaiser@tut.fi)

About: An implementation of MROGH descriptor. For more information, please refer to: “Bin Fan, Fuchao Wu and Zhanyi Hu, Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor, CVPR 2011, pp.2377-2384.” The most up-to-date information can be found at : http://vision.ia.ac.cn/Students/bfan/index.htm

Initial Announcement on mloss.org.

About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar.

This version introduces a number of new features:

• The parser does not depend anymore on CPLEX (or any other non-free LP solver). Instead, the decoder is now based on AD3, our free library for approximate MAP inference.

• The parser now outputs dependency labels along with the backbone structure.

• As a bonus, we now provide a trainable part-of-speech tagger, called TurboTagger, which can be used in standalone mode, or to provide part-of-speech tags as input for the parser. TurboTagger has state-of-the-art accuracy for English (97.3% on section 23 of the Penn Treebank) and is fast (~40,000 tokens per second).

• The parser is much faster than in previous versions. You may choose among a basic arc-factored parser (~4,300 tokens per second), a standard second-order model with consecutive sibling and grandparent features (the default; ~1,200 tokens per second), and a full model with head bigram and arbitrary sibling features (~900 tokens per second).

Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package.

This software has been tested on Linux, but it should run in other platforms with minor adaptations.

About: The VLFeat open source library implements popular computer vision algorithms including affine covariant feature detectors, HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.16.

VLFeat 0.9.16: Added VL_COVDET() (covariant feature detectors). This function implements the following detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also implements affine adaptation, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet the auxiliary function VL_PLOTSS(). This is the second point update supported by the PASCAL Harvest programme.

VLFeat 0.9.15: Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a vastly improved SVM implementation. Added IHASHSUM (hashed counting). Improved INTHIST (integral histogram). Added VL_CUMMAX(). Improved the implementation of VL_ROC() and VL_PR(). Added VL_DET() (Detection Error Trade-off (DET) curves). Improved the verbosity control to AIB. Added support for Xcode 4.3, improved support for past and future Xcode versions. Completed the migration of the old test code in toolbox/test, moving the functionality to the new unit tests toolbox/xtest. Improved credits. This is the first point update supported by the PASCAL Harvest (several more to come shortly).

About: This package is an implementation of a linear svm solver with a wide class of regularizations on the svm weight vector (l1, l2, mixed norm l1-lq, adaptive lasso). We provide solvers for the classical single task svm problem and for multi-task with joint feature selection or similarity promoting term.

Initial Announcement on mloss.org.

About: MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

What's new in version 3.3?

• support sklearn versions up to 0.12
• cleanly support reload
• fail gracefully if pp server does not start
• several bug-fixes and improvements

About: Likelihood-based Boosting for Generalized mixed models

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About: THIS VERSION DISCONTINUED, see "http://mloss.org/software/view/424/". This library provides ways of computing generalised 2nd-order Stirling numbers for Pitman-Yor and Dirichlet processes. Included is a tester and parameter optimiser. This accompanies Buntine and Hutter's article: http://arxiv.org/abs/1007.0296

See the alternative MLOSS entry "libstb". Updated to 1.4!

About: svm-struct-matlab is a MATLAB wrapper of T. Joachims' SVM^struct solver for structured output support vector machines.

Adds support for Xcode 4.0 and Mac OS X 10.7 and greater.

About: PLEASD: A Matlab Toolbox for Structured Learning

Initial Announcement on mloss.org.

About: Text-to-Speech (TTS) is a kind of speech processing technology that converts text into speech. It involves phonetics, linguistics, digital signal processing technology, computer technology, multimedia technology, and other technologies. It is a frontier technology in Chinese information processing field. With TTS technology, any text used to be read by eyes can also be listened by ears.

Initial Announcement on mloss.org.

About: A general purpose library to process and predict sequences of elements using echo state networks.

Initial Announcement on mloss.org.

About: An open source Java software providing collaborative filtering algorithms.

Initial Announcement on mloss.org.

About: "Pattern" is a web mining module for Python. It bundles tools for data retrieval, text analysis, clustering and classification, and data visualization.

• Small bug fixes in overall + performance improvements.
• Module pattern.web: updated to the new Bing API (Bing API has is paid service now).
• Module pattern.en: now includes Norvig's spell checking algorithm.
• Module pattern.de: new German tagger/chunker, courtesy of Schneider & Volk (1998) who kindly agreed to release their work in Pattern under BSD.
• Module pattern.search: the search syntax now includes { } syntax to define match groups.
• Module pattern.vector: fast implementation of information gain for feature selection.
• Module pattern.graph: now includes a toy semantic network of commonsense (see examples).
• Module canvas.js: image pixel effects & editor now supports live editing

About: The OrGanic Environment for Reservoir computing (Oger) toolbox is a Python toolbox for rapidly building, training and evaluating modular learning architectures on large datasets.

Initial Announcement on mloss.org.

About: Shrunken Centroids Regularized Discriminant Analysis

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