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About: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS) Changes:Fetched by rcranrobot on 20120201 00:00:11.194183

About: MATLAB toolbox for advanced BrainComputer Interface (BCI) research. Changes:Initial Announcement on mloss.org.

About: This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the hightreewidth setting. Changes:Initial Announcement on mloss.org.

About: Nonnegative Sparse Coding, Discriminative Semisupervised Learning, sparse probability graph Changes:Initial Announcement on mloss.org.

About: The KernelMachine Library is a free (released under the LGPL) C++ library to promote the use of and progress of kernel machines. Changes:Updated mloss entry (minor fixes).

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

About: An annotated java framework for machine learning, aimed at making it really easy to access analytically functions. Changes:Now supports OLS and GLS regression and NaiveBayes classification

About: In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEntPCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction error either based on L2norm or L1norm, the MaxEntPCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEntPCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEntPCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on realworld datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods. Changes:Initial Announcement on mloss.org.

About: MetropolisHastings alogrithm is a Markov chain Monte Carlo method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. Thi sequence can be used to approximate the distribution. Changes:Initial Announcement on mloss.org.

About: This code is developed based on Uriel Roque's active set algorithm for the linear least squares problem with nonnegative variables in: Portugal, L.; Judice, J.; and Vicente, L. 1994. A comparison of block pivoting and interiorpoint algorithms for linear least squares problems with nonnegative variables. Mathematics of Computation 63(208):625643.Ran He, WeiShi Zheng and Baogang Hu, "Maximum Correntropy Criterion for Robust Face Recognition," IEEE TPAMI, in press, 2011. Changes:Initial Announcement on mloss.org.
