Bibliographic Details
| Title: |
Regularization Techniques and Suboptimal Solutions to Optimization Problems in Learning from Data. |
| Authors: |
Gnecco, Giorgio |
| Source: |
Neural Computation. Mar2010, Vol. 22 Issue 3, p793-829. 37p. 1 Chart. |
| Subjects: |
Hilbert space, Structural optimization, Approximation theory, Systemic memory hypothesis, Automatic hypothesis formation, Experimental design |
| Abstract: |
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. [ABSTRACT FROM AUTHOR] |
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| Database: |
Psychology and Behavioral Sciences Collection |
