NDLI: A novel approach to optimize the objective function based on VC dimension and structural risk minimization

Content Provider	IEEE Xplore Digital Library
Author	Qiu Xintao Fu Dongmei Yang Tao
Copyright Year	2011
Description	Author affiliation: School of Automation, University of Science and Technology Beijing, 100083, China (Qiu Xintao; Fu Dongmei; Yang Tao)
Abstract	So far, lots of algorithms and learning methods are taking the empirical risk for the optimization goal. In this paper, we propose a new way to optimize the objective function based on VC dimension and structural risk minimization. The optimized function F is firstly defined by us, and some effective design forms of it will also be given. Then we fulfill a useful criterion—the balance minimum optimization principle. This principle considers not only the empirical risk, but also considers the VC dimension of learning machine. Balancing the two factors can avoid the underfitting problem and overfitting problem. Experimental results show that the method we proposed is effective to improve the property of algorithm efficiency, convergence and generalization of the learning machine. Also, the proposed principle for optimization is a new criterion, which is not a practical method for a particular problem of improvement. Therefore, this method is suitable to be applied in many practical situations, which may bring a good generalization performance and efficiency in some learning problems.
Starting Page	3226
Ending Page	3230
File Size	91053
Page Count	5
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Algorithm design and analysis Support vector machines Objective Function Statistical learning VC Dimension Risk management Balance Minimum Optimization Principle Optimization Genetic algorithms Structural Risk Minimization
Content Type	Text
Resource Type	Article

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