Training v-Support Vector Regression: Theory and Algorithms
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Publication:3149528
DOI10.1162/089976602760128081zbMath1010.68114OpenAlexW2115076892WikidataQ52034609 ScholiaQ52034609MaRDI QIDQ3149528
Chih-Chung Chang, Chih-Jen Lin
Publication date: 25 September 2002
Published in: Neural Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1162/089976602760128081
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Cites Work
- Training v-Support Vector Classifiers: Theory and Algorithms
- Efficient SVM regression training with SMO
- A simple decomposition method for support vector machines
- Feasible direction decomposition algorithms for training support vector machines
- Convergence of a generalized SMO algorithm for SVM classifier design
