Showing Items 191200 of 619 on page 20 of 62: First Previous 15 16 17 18 19 20 21 22 23 24 25 Next Last
About: Matlab code for semisupervised regression and dimensionality reduction using Hessian energy. Changes:Initial Announcement on mloss.org.

About: HSSVM is a software for solving multiclass problem using Hypersphere Support Vector Machines model, implemented by Java. Changes:

About: Hubnessaware Machine Learning for Highdimensional Data Changes:

About: Hype is a proofofconcept deep learning library, where you can perform optimization on compositional machine learning systems of many components, even when such components themselves internally perform optimization. Changes:Initial Announcement on mloss.org.

About: Itemset boosting (iBoost) performs linear regression in the complete space of power sets of mutations. It implements a forward feature selection procedure where, in each iteration, one mutation [...] Changes:Initial Announcement on mloss.org.

About: hapFabia is an R package for identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data. Changes:o citation update o plot function improved

About: Intended for .NET developers wanting to implement algorithms directly in a common .NET language (recommended: C#). Support for ndim generic arrays, LAPACK, FFT, cells, logicals, 2D&3D plotting [...] Changes:Initial Announcement on mloss.org.

About: This package includes implementations of the CCM, DMV and DMV+CCM parsers from Klein and Manning (2004), and code for testing them with the WSJ, Negra and Cast3LB corpuses (English, German and Spanish respectively). A detailed description of the parsers can be found in Klein (2005). Changes:Initial Announcement on mloss.org.

About: The incomplete Cholesky decomposition for a dense symmetric positive definite matrix A is a simple way of approximating A by a matrix of low rank (you can choose the rank). It has been used [...] Changes:Initial Announcement on mloss.org.

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. Changes:
