In this paper we present a new type of binary classifier defined on the unit cube. This classifier combines some of the aspects of the standard methods that have been used in the logical analysis of data (LAD) and geometric classifiers, with a nearest-neighbor paradigm. We assess the predictive performance of the new classifier in learning from a sample, obtaining generalization error bounds that improve as the 'sample width' of the classifier increases.
|State||Published - 1 Dec 2012|
|Event||International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012 - Fort Lauderdale, FL, United States|
Duration: 9 Jan 2012 → 11 Jan 2012
|Conference||International Symposium on Artificial Intelligence and Mathematics, ISAIM 2012|
|City||Fort Lauderdale, FL|
|Period||9/01/12 → 11/01/12|