About: Fitting user specified models with Group Lasso penalty

Fetched by r-cran-robot on 2013-04-01 00:00:05.428021

About: The goal of this project is to provide code for reading and writing machine learning data sets for as many programming languages as possible.

Forgot to include the Java sources.

About: This code is provided by Jun Wan. It is used in the Chalearn one-shot learning gesture challenge (round 2). This code includes: bag of features, 3D MoSIFT-based features (i.e. 3D MoSIFT, 3D EMoSIFT and 3D SMoSIFT), and the MFSK feature.

Initial Announcement on mloss.org.

About: 3-layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM.

Initial Announcement on mloss.org.

About: Aleph is both a multi-platform machine learning framework aimed at simplicity and performance, and a library of selected state-of-the-art algorithms.

Initial Announcement on mloss.org.

About: Encog is a Machine Learning framework for Java, C#, Javascript and C/C++ that supports SVM's, Genetic Programming, Bayesian Networks, Hidden Markov Models and other algorithms.

Changes for Encog 3.2:

Issue #53: Fix Out Of Range Bug In BasicMLSequenceSet. Issue #52: Unhandled exception in Encog.Util.File.ResourceLoader.CreateStream (ResourceLoader.cs) Issue #50: Concurrency bugs in PruneIncremental Issue #48: Unit Tests Failing - TestHessian Issue #46: Couple of small fixes - Temporal DataSet and SCG training Issue #45: Fixed EndMinutesStrategy to correctly evaluate ShouldStop after the specified number of minutes have elapsed. Issue #44: Encog.ML.Data.Basic.BasicMLDataPairCentroid.Add() & .Remove() Issue #43: Unit Tests Failing - Matrix not full rank Issue #42: Nuget - NuSpec Issue #36: Load Examples easier

About: A Toolkit for Recursive Partytioning

Fetched by r-cran-robot on 2013-04-01 00:00:06.838561

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: Hubness-aware Machine Learning for High-dimensional Data

• BibTex support for all algorithm implementations, making all of them easy to reference (via algref package).

• Two more hubness-aware approaches (meta-metric-learning and feature construction)

• An implementation of Hit-Miss networks for analysis.

• Several minor bug fixes.

• The following instance selection methods were added: HMScore, Carving, Iterative Case Filtering, ENRBF.

• The following clustering quality indexes were added: Folkes-Mallows, Calinski-Harabasz, PBM, G+, Tau, Point-Biserial, Hubert's statistic, McClain-Rao, C-root-k.

• Some more experimental scripts have been included.

• Extensions in the estimation of hubness risk.

• Alias and weighted reservoir methods for weight-proportional random selection.

About: The Kernel-Machine Library is a free (released under the LGPL) C++ library to promote the use of and progress of kernel machines.

Updated mloss entry (minor fixes).

About: The SimpleSVM toolbox contains the svm solver of the same name. The current version includes C-SVM, HM-SVM and nu-SVM based on the regularization path. It will soon include OC-SVM, regularization [...]

Initial Announcement on mloss.org.

About: Jubatus is a general framework library for online and distributed machine learning. It currently supports classification, regression, clustering, recommendation, nearest neighbors, anomaly detection, and graph analysis. Loose model sharing provides higher scalability, better performance, and real-time capabilities, by combining online learning with distributed computations.

0.5.0 add new supports for clustering and nearest neighbors. For more detail, see http://t.co/flMcTcYZVs

About: Visualizing the performance of scoring classifiers.

Fetched by r-cran-robot on 2013-04-01 00:00:08.344833

About: Likelihood-based Boosting for Generalized mixed models

Fetched by r-cran-robot on 2013-04-01 00:00:05.366545

About: This is a C++ software designed to train large-scale SVMs for binary classification. The algorithm is also implemented in parallel (**PGPDT**) for distributed memory, strictly coupled multiprocessor [...]

Initial Announcement on mloss.org.

About: Java package implementing a kernel for (molecular) graphs based on iterative graph similarity and optimal assignments.

Initial Announcement on mloss.org.

About: Shrinkage Discriminant Analysis and CAT Score Variable Selection

Fetched by r-cran-robot on 2012-02-01 00:00:11.559491

About: Stochastic neighbor embedding originally aims at the reconstruction of given distance relations in a low-dimensional Euclidean space. This can be regarded as general approach to multi-dimensional scaling, but the reconstruction is based on the definition of input (and output) neighborhood probability alone. The present implementation also allows for handling dissimilarity or score-induced neighborhood topologies and makes use of quasi 2nd order gradient-based (l-)BFGS optimization.

• gradient in xsne_fun.m fixed! (constant factor m was missing)

• symmetry option re-introduced allowing for enabling symmetric and asymmetric versions of SNE and t-SNE

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 [...]

Initial Announcement on mloss.org.