Artifacts using Weka Dev (257)

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ImgLib2 Labkit

Class for building pace regression linear models and using them for prediction. Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions. The current work of the pace regression theory, and therefore also this implementation, do not handle: - missing values - non-binary nominal attributes - the case that n - k is small where n is ...

Module for WEKA code (long term support).

A Boolean Matrix Decomposition Framework

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.

processors

This package provides generic configuration class and distributed map/reduce style tasks for Weka

MaryTTS Builder

WekaExcel adds support to directory read from and write to spreadsheets in Microsoft Excel 97-2007 format. It uses Apache POI (http://poi.apache.org/), specifically POI-HSSF and POI-XSSF (http://poi.apache.org/spreadsheet/), in order to read/write Excel spreadsheets.

Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region. The decision between the children of each center and itself is done comparing the BIC-values of the two structures. For more information see: Dan Pelleg, Andrew W. Moore: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Seventeenth International Conference on Machine Learning, 727-734, ...