NDLI: Kernel-based mixture of experts models for linear regression

Content Provider	IEEE Xplore Digital Library
Author	Santarcangelo, J. Xiao-Ping Zhang
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada (Santarcangelo, J.; Xiao-Ping Zhang)
Abstract	This paper proposes a novel kernel-based mixture of experts model for linear regression. The method is novel in that it formulates the mixture of experts model for linear regression so that kernel functions can be used. This allows the method to work directly in terms of kernels and avoids the explicit introduction of the feature vector, allowing one to use feature spaces of high, even infinite dimensionality. Other advantages of the model include the ability to take advantage of all the work related to kernels, a closed-form solution for maximization, as well as maintaining all the advantages of a linear expert. In this paper the supervised version is formulated. The model is verified and tested with simulated data. It was also found that the model had overall better performance than standard mixture of experts for regression on the well-known Boston Housing data set. Kernels used included polynomial, radial basis function and the ANOVA kernel.
Starting Page	1526
Ending Page	1529
File Size	329158
Page Count	4
File Format	PDF
ISBN	9781479983919
DOI	10.1109/ISCAS.2015.7168936
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-24
Publisher Place	Portugal
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Polynomials Mathematical model Linear regression Data models Computational modeling Training neural network linear regression mixture of experts kernels
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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