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About: A library for calculating and accessing generalized Stirling numbers of the second kind, which are used for inference in Poisson-Dirichlet processes. Changes:Initial Announcement on mloss.org.
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About: The original Random Forests implementation by Breiman and Cutler. Changes:Initial Announcement on mloss.org.
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About: ALgebraic COmbinatorial COmpletion of MAtrices. A collection of algorithms to impute or denoise single entries in an incomplete rank one matrix, to determine for which entries this is possible with any algorithm, and to provide algorithm-independent error estimates. Includes demo scripts. Changes:Initial Announcement on mloss.org.
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About: An implementation of MROGH descriptor. For more information, please refer to: “Bin Fan, Fuchao Wu and Zhanyi Hu, Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor, CVPR 2011, pp.2377-2384.” The most up-to-date information can be found at : http://vision.ia.ac.cn/Students/bfan/index.htm Changes:Initial Announcement on mloss.org.
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About: FsAlg is a linear algebra library that supports generic types. Changes:Initial Announcement on mloss.org.
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About: A MATLAB toolbox for defining complex machine learning comparisons Changes:Initial Announcement on mloss.org.
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About: Recur is a collection of Gstreamer plugins and language modelling tools based on recurrent neural networks. Changes:Initial Announcement on mloss.org.
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About: This program is a C++ implementation of Naive Bayes Classifier, which is a well-known generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. The program uses the multinomial event model for representation, the maximum likelihood estimate with a Laplace smoothing technique for learning parameters. A sparse-data structure is defined to represent the feature vector in the program to seek higher computational speed. Changes:Initial Announcement on mloss.org.
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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: A Theano framework for building and training neural networks Changes:Initial Announcement on mloss.org.
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About: This library implements the Optimum-Path Forest classifier for unsupervised and supervised learning. Changes:Initial Announcement on mloss.org.
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About: A method to optimize the hyperparameters for machine learning methods implemented in Scikit-learn based on Derivative Free Optimization Changes:Initial Announcement on mloss.org.
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About: MALSS is a python module to facilitate machine learning tasks. Changes:Initial Announcement on mloss.org.
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About: Software for graph similarity search for massive graph databases Changes:Initial Announcement on mloss.org.
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About: Python toolbox for manifold optimization with support for automatic differentiation Changes:Initial Announcement on mloss.org.
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About: In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEnt-PCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction error either based on L2-norm or L1-norm, the MaxEnt-PCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEnt-PCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEnt-PCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods. Changes:Initial Announcement on mloss.org.
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About: Survival forests: Random Forests variant for survival analysis. Original implementation by Leo Breiman. Changes:Initial Announcement on mloss.org.
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About: Code for Calibrated AdaMEC for binary cost-sensitive classification. The method is just AdaBoost that properly calibrates its probability estimates and uses a cost-sensitive (i.e. risk-minimizing) decision threshold to classify new data. Changes:Updated license information
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About: The package computes the optimal parameters for the Choquet kernel Changes:Initial Announcement on mloss.org.
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About: Learning Discrete Bayesian Network Classifiers from Data Changes:Fetched by r-cran-robot on 2016-05-01 00:00:04.546512
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