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About: Logic Regression Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.139495
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About: MLwizard recommends and optimizes classification algorithms based on meta-learning and is a software wizard fully integrated into RapidMiner but can be used as library as well. Changes:Faster parameter optimization using genetic algorithm with predefined start population.
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About: Matlab code for learning probabilistic SVM in the presence of uncertain labels. Changes:Added missing dataset function (thanks to Hao Wu)
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About: A Matlab implementation of Uncorrelated Multilinear Discriminant Analysis (UMLDA) for dimensionality reduction of tensor data via tensor-to-vector projection Changes:Initial Announcement on mloss.org.
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About: This package contains a python and a matlab implementation of the most widely used algorithms for multi-armed bandit problems. The purpose of this package is to provide simple environments for comparison and numerical evaluation of policies. Changes:Initial Announcement on mloss.org.
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About: Quantile Regression Forests Changes:Fetched by r-cran-robot on 2013-04-01 00:00:07.576421
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About: Relaxed Lasso Changes:Fetched by r-cran-robot on 2013-04-01 00:00:07.978325
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About: Elastic-Net for Sparse Estimation and Sparse PCA Changes:Fetched by r-cran-robot on 2013-04-01 00:00:04.831694
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About: We study the problem of robust feature extraction based on L21 regularized correntropy in both theoretical and algorithmic manner. In theoretical part, we point out that an L21-norm minimization can be justified from the viewpoint of half-quadratic (HQ) optimization, which facilitates convergence study and algorithmic development. In particular, a general formulation is accordingly proposed to unify L1-norm and L21-norm minimization within a common framework. In algorithmic part, we propose an L21 regularized correntropy algorithm to extract informative features meanwhile to remove outliers from training data. A new alternate minimization algorithm is also developed to optimize the non-convex correntropy objective. In terms of face recognition, we apply the proposed method to obtain an appearance-based model, called Sparse-Fisherfaces. Extensive experiments show that our method can select robust and sparse features, and outperforms several state-of-the-art subspace methods on largescale and open face recognition datasets. Changes:Initial Announcement on mloss.org. |
About: A Matlab implementation of Uncorrelated Multilinear PCA (UMPCA) for dimensionality reduction of tensor data via tensor-to-vector projection Changes:Initial Announcement on mloss.org.
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About: The code is for computing state-of-the-art video descriptors for action recognition. The most up-to-date information can be found at: http://lear.inrialpes.fr/people/wang/dense_trajectories Changes:Initial Announcement on mloss.org.
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About: Ran He, Wei-Shi Zheng,Tieniu Tan, and Zhenan Sun. Half-quadratic based Iterative Minimization for Robust Sparse Representation. Submitted to IEEE Trans. on Pattern Analysis and Machine Intelligence. Changes:Initial Announcement on mloss.org.
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About: A C++ Library for Discrete Graphical Models Changes:Initial Announcement on mloss.org.
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About: The package provides a Lagrangian approach to the posterior regularization of given linear mappings. This is important in two cases, (a) when systems are under-determined and (b) when the external model for calculating the mapping is invariant to properties such as scaling. The software may be applied in cases when the external model does not provide its own regularization strategy. In addition, the package allows to rank attributes according to their distortion potential to a given linear mapping. Changes:Version 1.1 (May 23, 2012) memory and time optimizations distderivrel.m now supports assessing the relevance of attribute pairs Version 1.0 (Nov 9, 2011) * Initial Announcement on mloss.org.
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About: Use the power of crowdsourcing to create ensembles. Changes:Initial Announcement on mloss.org.
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About: Oblique Trees for Classification Data Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.648184
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About: Fast C++ implementation of the variation of information (Meila 2003) and Rand index (Rand 1971) with MATLAB mex files Changes:Initial Announcement on mloss.org.
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About: This archive contains a Matlab implementation of the Multilinear Principal Component Analysis (MPCA) algorithm and MPCA+LDA, as described in the paper Haiping Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, "MPCA: Multilinear Principal Component Analysis of Tensor Objects", IEEE Transactions on Neural Networks, Vol. 19, No. 1, Page: 18-39, January 2008. Changes:Initial Announcement on mloss.org.
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