About: Heteroscedastic Discriminant Analysis

Fetched by r-cran-robot on 2013-04-01 00:00:05.551691

About: JProGraM (PRObabilistic GRAphical Models in Java) is a statistical machine learning library. It supports statistical modeling and data analysis along three main directions: (1) probabilistic graphical models (Bayesian networks, Markov random fields, dependency networks, hybrid random fields); (2) parametric, semiparametric, and nonparametric density estimation (Gaussian models, nonparanormal estimators, Parzen windows, Nadaraya-Watson estimator); (3) generative models for random networks (small-world, scale-free, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition.

JProGraM 13.2 -- CHANGE LOG

Release date: February 13, 2012

New features: -- Support for Fiedler random graphs/random field models for large-scale networks (ninofreno.graph.fiedler package); -- Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).

About: Regularization paths for regression models with grouped covariates

Fetched by r-cran-robot on 2013-04-01 00:00:05.489694

About: Model-Based Boosting

Fetched by r-cran-robot on 2013-04-01 00:00:06.324985

About: L1 Regularization Path for Generalized Linear Models and Cox Proportional Hazards Model

Fetched by r-cran-robot on 2013-04-01 00:00:05.305206

About: Generalized Boosted Regression Models

Fetched by r-cran-robot on 2013-04-01 00:00:05.019963

About: A Laboratory for Recursive Partytioning

Fetched by r-cran-robot on 2013-04-01 00:00:06.775432

About: Classification rule based on Bayesian naive Bayes models with feature selection bias corrected

Fetched by r-cran-robot on 2012-12-01 00:00:07.510624

About: Bayesian Prediction with High-order Interactions

Fetched by r-cran-robot on 2012-12-01 00:00:03.777292

About: Mapping, pruning, and graphing tree models

Fetched by r-cran-robot on 2013-04-01 00:00:06.263217

About: L1 constrained estimation aka `lasso'

Fetched by r-cran-robot on 2013-04-01 00:00:05.967868

About: Improved Predictors

Fetched by r-cran-robot on 2013-04-01 00:00:05.613011

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: L1 (lasso and fused lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model

Fetched by r-cran-robot on 2013-04-01 00:00:06.939105

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

Fetched by r-cran-robot on 2013-04-01 00:00:07.638240

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: Simpler use of data mining methods (e.g. NN and SVM) in classification and regression.

Fetched by r-cran-robot on 2013-04-01 00:00:08.226306

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

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