Description: SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping
h: X--> Y
using labeled training examples (x1,y1), ..., (xn,yn). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVMstruct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel.
- [2007fs-svm] - New vector machine SVM source, IEEE publ
- [qpso-svm] - This procedure acts with quantum particl
- [kalman2] - Kalman filtering of small procedures, to
- [svm_perf] - SVMstruct is a Support Vector Machine (S
- [kw] - Literature and kalman filtering based on
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