About: The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents. Changes:

About: Breiman and Cutler's random forests for classification and regression Changes:Fetched by rcranrobot on 20130401 00:00:07.638240

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: Pyriel is a Python system for learning classification rules from data. Unlike other rule learning systems, it is designed to learn rule lists that maximize the area under the ROC curve (AUC) instead of accuracy. Pyriel is mostly an experimental research tool, but it's robust and fast enough to be used for lightweight industrial data mining. Changes:1.5 Changed CF (confidence factor) to do LaPlace smoothing of estimates. New flag "scoreforclass C" causes scores to be computed relative to a given (positive) class. For twoclass problems. Fixed bug in example sampling code (sample n) Fixed bug keeping oldstyle example formats (terminated by dot) from working. More code restructuring.

About: C++ software for statistical classification, probability estimation and interpolation/nonlinear regression using variable bandwidth kernel estimation. Changes:New in Version 0.9.8:

About: Bayesian treed Gaussian process models Changes:Fetched by rcranrobot on 20120201 00:00:11.834310

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, NadarayaWatson estimator); (3) generative models for random networks (smallworld, scalefree, exponential random graphs, Fiedler random graphs/fields), subgraph sampling algorithms (random walk, snowball, etc.), and spectral decomposition. Changes:JProGraM 13.2  CHANGE LOG Release date: February 13, 2012 New features:  Support for Fiedler random graphs/random field models for largescale networks (ninofreno.graph.fiedler package);  Various bugfixes and enhancements (especially in the ninofreno.graph and ninofreno.math package).

About: This package contains a python and a matlab implementation of the most widely used algorithms for multiarmed bandit problems. The purpose of this package is to provide simple environments for comparison and numerical evaluation of policies. Changes:Initial Announcement on mloss.org.

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems Changes:

About: KernelBased Analysis of Biological Sequences Changes:
