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Adaboost extensions for cost-sentive classification CSExtension 1 CSExtension 2 CSExtension 3 CSExtension 4 CSExtension 5 AdaCost Boost CostBoost Uboost CostUBoost AdaBoostM1 Implementation of all the listed algorithms of the cluster "cost-sensitive classification". They are the meta algorithms which requires base algorithms e.g. Decision Tree Moreover, Voting criteria is also required e.g. Minimum expected cost criteria Input also requires to load an arff file and a cost matrix (sample arff and cost files are uploaded for users' reference) This extension uses weka for classification and generates the classification model along with confusion matrix. For given dataset and cost matrix

Free Open Source VoiceXML editor programmed in Java (Swing). The VoiceXML document is regularly parsed, a tree view is built and syntax errors are reported in a specific table.

A flexible Japanese to English language translation engine that allows multiple results be displayed according to all possible grammar ratings, i.e. a list of results will be displayed instead of only one. Please browse the CVS tree for actual project.

A ContextTree is a way of dynamically forming relationships between information: the same information can be viewed in different ways, depending on what you want from it.