Nonlinear programming for classification problems in machine learning

We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis,...

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Bibliographic Details
Main Authors: Astorino, Annabella, Fuduli, Antonio, Gaudioso, Manlio
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Classification
Deoxyribonucleic acid
DNA
Gene expression
Machine learning
Machine vision
Mathematical models
Nonlinear programming
Object recognition
Optimization
Proteins
Separation
Support vector machines
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Summary:We survey some nonlinear models for classification problems arising in machine learning. In the last years this field has become more and more relevant due to a lot of practical applications, such as text and web classification, object recognition in machine vision, gene expression profile analysis, DNA and protein analysis, medical diagnosis, customer profiling etc. Classification deals with separation of sets by means of appropriate separation surfaces, which is generally obtained by solving a numerical optimization model. While linear separability is the basis of the most popular approach to classification, the Support Vector Machine (SVM), in the recent years using nonlinear separating surfaces has received some attention. The objective of this work is to recall some of such proposals, mainly in terms of the numerical optimization models. In particular we tackle the polyhedral, ellipsoidal, spherical and conical separation approaches and, for some of them, we also consider the semisupervised versions.
ISSN:0094-243X
1551-7616
DOI:10.1063/1.4965316