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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: GritBot is an data cleaning and outlier/anomaly detection program. Changes:Initial Announcement on mloss.org.

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

About: A python implementation of Breiman's Random Forests. Changes:Initial Announcement on mloss.org.

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

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

About: This program is a C++ implementation of Naive Bayes Classifier, which is a wellknown generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. The program uses the multinomial event model for representation, the maximum likelihood estimate with a Laplace smoothing technique for learning parameters. A sparsedata structure is defined to represent the feature vector in the program to seek higher computational speed. Changes:Initial Announcement on mloss.org.

About: This is a class to calculate histogram of LBP (local binary patterns) from an input image, histograms of LBPTOP (local binary patterns on three orthogonal planes) from an image sequence, histogram of the rotation invariant VLBP (volume local binary patterns) or uniform rotation invariant VLBP from an image sequence. Changes:Initial Announcement on mloss.org.

About: PyStruct is a framework for learning structured prediction in Python. It has a modular interface, similar to the wellknown SVMstruct. Apart from learning algorithms it also contains model formulations for popular CRFs and interfaces to many inference algorithm implementation. Changes:Initial Announcement on mloss.org.

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.
