Nonlinear separation of data via Mixed 0–1 Integer and Linear Programming

This paper presents a new mathematical programming-based learning methodology for separation of two types of data. Specifically, we develop a new l 1-norm error distance metric and use it to develop a Mixed 0–1 Integer and Linear Programming (MILP) model that optimizes the interplay of user-provided...

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Bibliographic Details
Published in:	Applied mathematics and computation Vol. 193; no. 1; pp. 183 - 196
Main Authors:	Kim, Kwangsoo, Ryoo, Hong Seo
Format:	Journal Article
Language:	English
Published:	New York, NY Elsevier Inc 01.10.2007 Elsevier
Subjects:	Binary classification Calculus of variations and optimal control Exact sciences and technology Global optimization Mathematical analysis Mathematics Mixed 0–1 integer and linear program Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Sciences and techniques of general use Supervised learning Mixed 0–1 integer and linear program Global optimization Supervised learning Binary classification Error estimation Optimization method Linear programming Mixed 0-1 integer and linear program Implementation Linear model Learning Integer Integer programming Kernel function Numerical analysis Applied mathematics Classification Database Two-dimensional calculations Mathematical programming
ISSN:	0096-3003, 1873-5649
Online Access:	Get full text
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