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About: The aim is to embed a given data relationship matrix into a low-dimensional Euclidean space such that the point distances / distance ranks correlate best with the original input relationships. Input relationships may be given as (sparse) (asymmetric) distance, dissimilarity, or (negative!) score matrices. Input-output relations are modeled as low-conditioned. (Weighted) Pearson and soft Spearman rank correlation, and unweighted soft Kendall correlation are supported correlation measures for input/output object neighborhood relationships. Changes:
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About: This is the core MCMC sampler for the nonparametric sparse factor analysis model presented in David A. Knowles and Zoubin Ghahramani (2011). Nonparametric Bayesian Sparse Factor Models with application to Gene Expression modelling. Annals of Applied Statistics Changes:Initial Announcement on mloss.org.
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About: Regularization paTH for LASSO problem (thalasso) thalasso solves problems of the following form: minimize 1/2||X*beta-y||^2 + lambda*sum|beta_i|, where X and y are problem data and beta and lambda are variables. Changes:Initial Announcement on mloss.org.
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About: The toolbox from the paper Near-optimal Experimental Design for Model Selection in Systems Biology (Busetto et al. 2013, submitted) implemented in MATLAB. Changes:Initial Announcement on mloss.org.
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About: Block-Coordinate Frank-Wolfe Optimization for Structural SVMs Changes:Initial Announcement on mloss.org.
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About: Bayesian state-space modelling and inference on high-performance computer hardware. Changes:Initial Announcement on mloss.org.
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About: This toolbox implements a novel visualization technique called Sectors on Sectors (SonS), and a extended version called Multidimensional Sectors on Sectors (MDSonS), for improving the interpretation of several data mining algorithms. The MDSonS method makes use of Multidimensional Scaling (MDS) to solve the main drawback of the previous method, namely, the lack of representing distances between pairs of clusters. These methods have been applied for visualizing the results of hierarchical clustering, Growing Hierarchical Self-Organizing Maps (GHSOM), classification trees and several manifolds. These methods make possible to extract all the existing relationships among centroids’ attributes at any hierarchy level. Changes:Initial Announcement on mloss.org.
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About: AISAIC software for analyzing human DNA copy numbers and detecting significant copy number alterations Changes:Initial Announcement on mloss.org.
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About: This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classicalmean square error (MSE) criterion that is sensitive to outliers. Then an l1 regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1 regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion. Changes:Initial Announcement on mloss.org.
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About: Robust sparse representation has shown significant potential in solving challenging problems in computer vision such as biometrics and visual surveillance. Although several robust sparse models have been proposed and promising results have been obtained, they are either for error correction or for error detection, and learning a general framework that systematically unifies these two aspects and explore their relation is still an open problem. In this paper, we develop a half-quadratic (HQ) framework to solve the robust sparse representation problem. By defining different kinds of half-quadratic functions, the proposed HQ framework is applicable to performing both error correction and error detection. More specifically, by using the additive form of HQ, we propose an L1-regularized error correction method by iteratively recovering corrupted data from errors incurred by noises and outliers; by using the multiplicative form of HQ, we propose an L1-regularized error detection method by learning from uncorrupted data iteratively. We also show that the L1-regularization solved by soft-thresholding function has a dual relationship to Huber M-estimator, which theoretically guarantees the performance of robust sparse representation in terms of M-estimation. Experiments on robust face recognition under severe occlusion and corruption validate our framework and findings. Changes:Initial Announcement on mloss.org.
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About: Regularization for semiparametric additive hazards regression Changes:Fetched by r-cran-robot on 2018-09-01 00:00:03.378832
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About: A fast and robust learning of Bayesian networks Changes:Initial Announcement on mloss.org.
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About: HLearn makes simple machine learning routines available in Haskell by expressing them according to their algebraic structure Changes:Updated to version 1.0
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About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in: - Ellipsoidal multiple instance learning - difference of convex functions algorithms for sparse classfication - Contextual bandits upper confidence bound algorithm (using GP) - learning output kernels, that is kernels between the labels of a classifier. Changes:
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About: A comprehensive data mining environment, with a variety of machine learning components. Changes:Modifications following feedback from Knime main Author.
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About: A descriptive and programming language independent format and API for the simplified configuration, documentation, and design of computer experiments. Changes:Initial Announcement on mloss.org.
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About: HDDM is a python toolbox for hierarchical Bayesian parameter estimation of the Drift Diffusion Model (via PyMC). Drift Diffusion Models are used widely in psychology and cognitive neuroscience to study decision making. Changes:
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About: R genetic programming framework Changes:Fetched by r-cran-robot on 2013-04-01 00:00:08.163887
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About: Pam Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.709586
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About: Generalized linear and additive models by likelihood based boosting Changes:Fetched by r-cran-robot on 2013-04-01 00:00:04.893311
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