Download JAR files tagged by gaussian with all dependencies

Methods for the extraction of local features. Local features are descriptions of regions of images (SIFT, ...) selected by detectors (Difference of Gaussian, Harris, ...).

Implements Bayesian Logistic Regression for both Gaussian and Laplace Priors. For more information, see Alexander Genkin, David D. Lewis, David Madigan (2004). Large-scale bayesian logistic regression for text categorization.

The rho mu library is a library of Randomization enHancements and Other Math Utilities. It includes implementations of various algorithms for randomly sampling indexes into arrays and other sequential structures, randomly sampling pairs and triples of unique indexes, randomly sampling k indexes, etc. It also includes efficient implementations of random number generation from distributions other than uniform, such as Gaussian, Cauchy, etc. Additionally, it includes implementations of other math functions that are either needed by the randomization utilities, or needed by some of our other projects.

Java implementation of the Ziggurat algorithm for generating Gaussian distributed pseudorandom numbers. The Ziggurat algorithm is significantly faster than the more commonly encountered Polar method, and has some other desirable statistical properties. The ZigguratGaussian class is a Java port of the GNU Scientific Library's C implementation (Voss, 2005) of the Ziggurat method. In porting to Java, we have made several optimizations, the details of which can be found in the source code comments, which highlights any differences between this Java implementation and the C implementation on which it is based. This package also includes an implementation of the Polar Method, included to enable comparing speed advantage of the Ziggurat algorithm.

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.