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An analysis engine to recognize mentions of gene point mutations in document text. This is a wrapper around the original MutationFinder (http://mutationfinder.sourceforge.net/), published in the following paper: MutationFinder: A high-performance system for extracting point mutation mentions from text J. Gregory Caporaso, William A. Baumgartner Jr., David A. Randolph, K. Bretonnel Cohen, and Lawrence Hunter; Bioinformatics, 2007 23(14):1862-1865; doi:10.1093/bioinformatics/btm235;

Class for building and using a Complement class Naive Bayes classifier. For more information see: Jason D. Rennie, Lawrence Shih, Jaime Teevan, David R. Karger: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: ICML, 616-623, 2003. P.S.: TF, IDF and length normalization transforms, as described in the paper, can be performed through weka.filters.unsupervised.StringToWordVector.

This package provides four search methods for attribute selection: ExhaustiveSearch, GeneticSearch, RandomSearch and RankSearch. See: David E. Goldberg (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley. Mark Hall, Geoffrey Holmes (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering. 15(6):1437-1447.

xml-commons provides an Apache-hosted set of DOM, SAX, and JAXP interfaces for use in other xml-based projects. Our hope is that we can standardize on both a common version and packaging scheme for these critical XML standards interfaces to make the lives of both our developers and users easier. The External Components portion of xml-commons contains interfaces that are defined by external standards organizations. For DOM, that's the W3C; for SAX it's David Megginson and sax.sourceforge.net; for JAXP it's Sun.

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001. Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.