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About: Soltion developed by team Turtle Tamers in the ChaLearn Gesture Challenge (http://www.kaggle.com/c/GestureChallenge2) Changes:Initial Announcement on mloss.org.

About: MLPlot is a lightweight plotting library written in Java. Changes:Initial Announcement on mloss.org.

About: Regression forests, Random Forests for regression. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.

About: STK++: A Statistical Toolkit Framework in C++ Changes:Inegrating openmp to the current release. Many enhancement in the clustering project. bug fix

About: Approximate Rank One FACtorization of tensors. An algorithm for factorization of threewaytensors and determination of their rank, includes example applications. Changes:Initial Announcement on mloss.org.

About: Bayesian statespace modelling and inference on highperformance computer hardware. Changes:Initial Announcement on mloss.org.

About: A general purpose library to process and predict sequences of elements using echo state networks. Changes:Initial Announcement on mloss.org.

About: Survival forests: Random Forests variant for survival analysis. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.

About: Software to perform isoline retrieval, retrieve isolines of an atmospheric parameter from a nadirlooking satellite. Changes:Added screenshot, keywords

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.
