期刊
NEURAL COMPUTATION
卷 22, 期 3, 页码 793-829出版社
MIT PRESS
DOI: 10.1162/neco.2009.05-08-786
关键词
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资金
- University of Genova
Various regularization techniques are investigated in supervised learning from data. Theoretical features of the associated optimization problems are studied, and sparse suboptimal solutions are searched for. Rates of approximate optimization are estimated for sequences of suboptimal solutions formed by linear combinations of n-tuples of computational units, and statistical learning bounds are derived. As hypothesis sets, reproducing kernel Hilbert spaces and their subsets are considered.
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