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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: A MATLAB toolkit for performing generalized regression with equality/inequality constraints on the function value/gradient. Changes:Initial Announcement on mloss.org.

About: This evaluation toolkit provides a unified framework for evaluating bagofwords based encoding methods over several standard image classification datasets. Changes:Initial Announcement on mloss.org.

About: Supervised Latent Semantic Indexing(SLSI) is an supervised feature transformation method. The algorithms in this package are based on the iterative algorithm of Latent Semantic Indexing. Changes:Initial Announcement on mloss.org.

About: SAMOA is a platform for mining big data streams. It is a distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms. Changes:Initial Announcement on mloss.org.

About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2X*betay^2 + lambda*sumbeta_i, where X and y are problem data and beta and lambda are variables. Changes:Initial Announcement on mloss.org.

About: CURFIL uses NVIDIA CUDA to accelerate random forest training and prediction for RGB and RGBD images. It focuses on image labelling tasks, such as image segmentation or classification applications. CURFIL allows to search for optimal hyperparameter configurations (e.g. using the hyperopt) package) by massively decreasing training time. Changes:Initial Announcement on mloss.org.

About: It is a Scala library for building Bayesian Networks with discrete/continuous variables and running deterministic Bayesian inference Changes:Initial Announcement on mloss.org.

About: LogRegCrowds is a collection of Julia implementations of various approaches for learning a logistic regression model multiple annotators and crowds, namely the works of Raykar et al. (2010), Rodrigues et al. (2013) and Dawid and Skene (1979). Changes:Initial Announcement on mloss.org. Added GitHub page.
