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Adapted infinite kernel learning by multi-local algorithm

Title	Adapted infinite kernel learning by multi-local algorithm
Author	Özöğür Akyüz, S., Üstünkar, Gürkan, Weber, G. W.
Publication Date:	2016-05
Publication Place	- World Scientific Publishing Co
Subject	Infinite kernel learning, Support vector machines, Optimization, Multi-local procedure, Multiple kernel learning, Simmulated annealing
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	1793-6381
Record ID	d89fd08e-ba26-4217-afe4-ec2bbaee161f
Date	2016-05
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, observed margin strategy is integrated into our novel infinite kernel learning (IKL) algorithm together with multi-local procedure (MLP) which is an optimization technique to find global solution. The experimental results show improvements in accuracy and speed when comparing with multiple kernel learning (MKL) and semi-infinite linear programming (SILP) with CV.
DOI	10.1142/S0218001416510046
Cilt	30

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Özyeğin University

Adapted infinite kernel learning by multi-local algorithm

Author Özöğür Akyüz, S., Üstünkar, Gürkan, Weber, G. W.

Publication Date 2016-05

Publication Place - World Scientific Publishing Co

Subject Infinite kernel learning, Support vector machines, Optimization, Multi-local procedure, Multiple kernel learning, Simmulated annealing

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 1793-6381

Record ID d89fd08e-ba26-4217-afe4-ec2bbaee161f

Date 2016-05

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text The interplay of machine learning (ML) and optimization methods is an emerging field of artificial intelligence. Both ML and optimization are concerned with modeling of systems related to real-world problems. Parameter selection for classification models is an important task for ML algorithms. In statistical learning theory, cross-validation (CV) which is the most well-known model selection method can be very time consuming for large data sets. One of the recent model selection techniques developed for support vector machines (SVMs) is based on the observed test point margins. In this study, observed margin strategy is integrated into our novel infinite kernel learning (IKL) algorithm together with multi-local procedure (MLP) which is an optimization technique to find global solution. The experimental results show improvements in accuracy and speed when comparing with multiple kernel learning (MKL) and semi-infinite linear programming (SILP) with CV.

DOI 10.1142/S0218001416510046

Cilt 30