In machine-learning, maximizing the sample margin can reduce the learning generalization-error. Thus samples on which the target function has a large margin (γ) convey more information so we expect fewer such samples. In this paper, we estimate the complexity of a class of sets of large-margin samples for a general learning problem over a finite domain. We obtain an explicit dependence of this complexity on γ and the sample size.
|Title of host publication||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Editors||Kyung-Yong Chwa, J. Ian Munro|
|Number of pages||12|
|ISBN (Electronic)||354022856X, 9783540228561|
|State||Published - 1 Jan 2004|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|