Showing Items 401410 of 550 on page 41 of 55: First Previous 36 37 38 39 40 41 42 43 44 45 46 Next Last
About: Feature Selection SVM using penalty functions Changes:Fetched by rcranrobot on 20130401 00:00:07.509844

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. Changes:New features:  R support that is now on CRAN

About: This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the hightreewidth setting. Changes:Initial Announcement on mloss.org.

About: Shrunken Centroids Regularized Discriminant Analysis Changes:Fetched by rcranrobot on 20130401 00:00:07.868841

About: In this paper, we propose an improved principal component analysis based on maximum entropy (MaxEnt) preservation, called MaxEntPCA, which is derived from a Parzen window estimation of Renyi’s quadratic entropy. Instead of minimizing the reconstruction error either based on L2norm or L1norm, the MaxEntPCA attempts to preserve as much as possible the uncertainty information of the data measured by entropy. The optimal solution of MaxEntPCA consists of the eigenvectors of a Laplacian probability matrix corresponding to the MaxEnt distribution. MaxEntPCA (1) is rotation invariant, (2) is free from any distribution assumption, and (3) is robust to outliers. Extensive experiments on realworld datasets demonstrate the effectiveness of the proposed linear method as compared to other related robust PCA methods. Changes:Initial Announcement on mloss.org.

About: This program is a C++ implementation of Naive Bayes Classifier, which is a wellknown 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 sparsedata structure is defined to represent the feature vector in the program to seek higher computational speed. Changes:Initial Announcement on mloss.org.

About: OpenPRNBEM is an C++ implementation of Naive Bayes Classifier, which is a wellknown generative classification algorithm for the application such as text classification. The Naive Bayes algorithm requires the probabilistic distribution to be discrete. OpenPRNBEM uses the multinomial event model for representation. The maximum likelihood estimate is used for supervised learning, and the expectationmaximization estimate is used for semisupervised and unsupervised learning. Changes:Initial Announcement on mloss.org.

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly nonlinear and nonconvex functions, using the CMAES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability. Changes:This is an important update:

About: PyStruct is a framework for learning structured prediction in Python. It has a modular interface, similar to the wellknown SVMstruct. Apart from learning algorithms it also contains model formulations for popular CRFs and interfaces to many inference algorithm implementation. Changes:Initial Announcement on mloss.org.

About: A Matlab implementation of Uncorrelated Multilinear Discriminant Analysis (UMLDA) for dimensionality reduction of tensor data via tensortovector projection Changes:Initial Announcement on mloss.org.
