Search Results for "svm classification"

Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying...

A set of java programs that extract coordinate and chemical information from PDB files. The binding site regions are extracted using grid based scheme. For binding site, spatio-chemical descriptor is generated based on PocketMatch algorithm of Dr. Kalidas (author of this project too).

The package provides SVM models to be used with SVMlight (http://svmlight.joachims.org/) for drawing a consensus out of individual 3rd-party predictions about the effect of mutations. The 3rd party tools are SIFT, Polyphen2, SNPs&GO and Mutation Assessor. The package also provides a series of Perl modules and scripts to assist in the preparation of data. The modules can be used to build custom tools and pipelines, whereas the scripts provide basic executable implementations based on the...

SVM# is a svm(support vector machine) classification implemented in C#. The project contains both train and predict modules.

This RapidMiner-plugin consists of operators for feature selection and classification - mainly on high-dimensional (microarray-) data - and some helper-classes/operators.

Feating constructs a classification ensemble comprising a set of local models. It is effective at reducing the error of both stable and unstable learners, including SVM. For details see the paper at http://dx.doi.org/10.1007/s10994-010-5224-5.

A standalone, STL interface to the Torch library's Support Vector Machine (SVM). It supports single or multiclass (one vs. all) classification using dot product, polynomial, Gaussian and sigmoid kernels.