About: Denoising images via normalized convolution Changes:Initial Announcement on mloss.org.

About: Classification and regression trees Changes:Fetched by rcranrobot on 20120201 00:00:11.999664

About: Bayesian Reasoning and Machine Learning toolbox Changes:Fixed some small bugs and updated some demos.

About: Regression Trees with Random Effects for Longitudinal (Panel) Data Changes:Fetched by rcranrobot on 20130401 00:00:08.040424

About: The Ktree is a scalable approach to clustering inspired by the B+tree and kmeans algorithms. Changes:Release of Ktree implementation in Python. This is targeted at a research and rapid prototyping audience.

About: A fast and scalable graphbased clustering algorithm based on the eigenvectors of the nonlinear 1Laplacian. Changes:

About: Rule and InstanceBased Regression Modeling Changes:Fetched by rcranrobot on 20110828 08:16:03.375532

About: Matlab implementation of variational gaussian approximate inference for Bayesian Generalized Linear Models. Changes:Code restructure and bug fix.

About: This is the source code of the mloss.org website. Changes:Now works with newer django versions and fixes several warnings and minor bugs underneath. The only user visible change is probably that the subscription and bookmark buttons work again.

About: A chatterbot that learns natural languages learning from imitation. Changes:Alpha 1  Codename: Wendell Borton ("Bllluuhhhhh...!!") Short term memory greatly improved.

About: Tools for functional network analysis. Changes:Initial Announcement on mloss.org.

About: A work in progress Changes:Initial Announcement on mloss.org.

About: Variable selection using random forests Changes:Fetched by rcranrobot on 20120201 00:00:12.245883

About: KeplerWeka represents the integration of all the functionality of the WEKA Machine Learning Workbench into the opensource scientific workflow Kepler. Among them are classification, [...] Changes:

About: Ordinal classification tree functions Changes:Initial Announcement on mloss.org by rcranrobot

About: The SUMO Toolbox is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (e.g., simulation code, data set, script, ...) within the accuracy and time constraints set by the user. The toolbox minimizes the number of data points (which it selects automatically) since they are usually expensive. Changes:Incremental update, fixing some cosmetic issues, coincides with JMLR publication.

About: The gmm toolbox contains code for density estimation using mixtures of Gaussians: Starting from simple kernel density estimation with spherical and diagonal Gaussian kernels over manifold Parzen window until mixtures of penalised full Gaussians with only a few components. The toolbox covers many Gaussian mixture model parametrisations from the recent literature. Most prominently, the package contains code to use the Gaussian Process Latent Variable Model for density estimation. Most of the code is written in Matlab 7.x including some MEX files. Changes:Initial Announcement on mloss.org

About: A stochastic variant of the mirror descent algorithm employing Langford and Zhang's truncated gradient idea to minimize L1 regularized loss minimization problems for classification and regression. Changes:Fixed major bug in implementation. The components of the iterate where the current example vector is zero were not being updated correctly. Thanks to Jonathan Chang for pointing out the error to us.

About: Feature Selection SVM using penalty functions Changes:Fetched by rcranrobot on 20130401 00:00:07.509844
