Adapted infinite kernel learning by multi-local algorithm

Name: Adapted infinite kernel learning by multi-local algorithm
Author: Özöğür Akyüz, S., Üstünkar, Gürkan, Weber, G. W.

İsim	Adapted infinite kernel learning by multi-local algorithm
Yazar	Özöğür Akyüz, S., Üstünkar, Gürkan, Weber, G. W.
Basım Tarihi:	2016-05
Basım Yeri	- World Scientific Publishing Co
Konu	Infinite kernel learning, Support vector machines, Optimization, Multi-local procedure, Multiple kernel learning, Simmulated annealing
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1793-6381
Kayıt Numarası	d89fd08e-ba26-4217-afe4-ec2bbaee161f
Tarih	2016-05
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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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Adapted infinite kernel learning by multi-local algorithm

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

Basım Tarihi 2016-05

Basım Yeri - World Scientific Publishing Co

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

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1793-6381

Kayıt Numarası d89fd08e-ba26-4217-afe4-ec2bbaee161f

Tarih 2016-05

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

Örnek Metin 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