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About: markov thebeast is a Markov Logic interpreter. We also see it as structured prediction framework in which the user can define a loglinear distribution over a complex output space. Changes:Initial Announcement on mloss.org.
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About: Fast Runtime-Flexible Multi-dimensional Arrays and Views for C++ Changes:Initial Announcement on mloss.org.
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About: A MATLAB spectral clustering package to deal with large data sets. Our tool can handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. Spectral clustering algorithm has been [...] Changes:
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About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others. Changes:
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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 interior-point algorithms for linear least squares problems with nonnegative variables. Mathematics of Computation 63(208):625-643.Ran He, Wei-Shi Zheng and Baogang Hu, "Maximum Correntropy Criterion for Robust Face Recognition," IEEE TPAMI, in press, 2011. Changes:Initial Announcement on mloss.org.
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About: We provide some preliminary code for multiclass multiple kernel learning in Matlab using CPLEX as a base solver. Changes:Initial Announcement on mloss.org.
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About: testing mloss.org Changes:Initial Announcement on mloss.org.
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About: MDP is a Python library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. The base of available algorithms includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others. Changes:What's new in version 3.3?
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About: Metropolis-Hastings 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.
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About: This toolbox implements models for Bayesian mixed-effects inference on classification performance in hierarchical classification analyses. Changes:In addition to the existing MATLAB implementation, the toolbox now also contains an R package of the variational Bayesian algorithm for mixed-effects inference.
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