About: Bayesian Prediction with Highorder Interactions Changes:Fetched by rcranrobot on 20121201 00:00:03.777292

About: Mapping, pruning, and graphing tree models Changes:Fetched by rcranrobot on 20130401 00:00:06.263217

About: L1 constrained estimation aka `lasso' Changes:Fetched by rcranrobot on 20130401 00:00:05.967868

About: Improved Predictors Changes:Fetched by rcranrobot on 20130401 00:00:05.613011

About: Python Machine Learning Toolkit Changes: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 Changes:Fetched by rcranrobot on 20130401 00:00:06.939105

About: Breiman and Cutler's random forests for classification and regression Changes:Fetched by rcranrobot on 20130401 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. Changes:This version introduces a number of new features:
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; googleglog, 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. Changes:Fetched by rcranrobot on 20130401 00:00:08.226306

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 preprocessing methods, and many others. Changes:What's new in version 3.3?

About: Likelihoodbased Boosting for Generalized mixed models Changes:Fetched by rcranrobot on 20130401 00:00:05.366545

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. Changes:Initial Announcement on mloss.org.

About: Logic Regression Changes:Fetched by rcranrobot on 20130401 00:00:06.139495

About: Shrunken Centroids Regularized Discriminant Analysis Changes:Fetched by rcranrobot on 20130401 00:00:07.868841

About: ElasticNet for Sparse Estimation and Sparse PCA Changes:Fetched by rcranrobot on 20130401 00:00:04.831694

About: R version of GENetic Optimization Using Derivatives Changes:Fetched by rcranrobot on 20130401 00:00:08.101900

About: A C++ Library for Discrete Graphical Models Changes:Initial Announcement on mloss.org.

About: Quantile Regression Forests Changes:Fetched by rcranrobot on 20130401 00:00:07.576421

About: Relaxed Lasso Changes:Fetched by rcranrobot on 20130401 00:00:07.978325
