![]() SVM struct: SVM learning for multivariate and structured outputs like trees, sequences, and sets (available here). ![]() Machine Learning Course: If you would like to learn more about Machine Learning, you can find videos, slides, and readings of the course I teach at Cornell here. supports standard kernel functions and lets you.handles several hundred-thousands of training examples.handles many thousands of support vectors.allows restarts from specified vector of dual variables.can train SVMs with cost models and example dependent costs.includes algorithm for approximately training large transductive SVMs (TSVMs) (see also Spectral Graph Transducer).efficiently computes Leave-One-Out estimates of theĮrror rate, the precision, and the recall.computes XiAlpha-estimates of the error rate, the precision, and the recall.learning retrieval functions in STRIVER search engine). For multivariate and structured outputs use SVM struct. solves classification and regression problems.working set selection based on steepest feasible.The main features of the program are the following: SVM light is an implementation of Support Vector Machines (SVMs) in C. University of Dortmund, Informatik, AI-UnitĬollaborative Research Center on 'Complexity Reduction in Multivariate Data' (SFB475)
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