NDLI: Busy stations recognition of Hangzhou public free-bicycle system based on sixth order polynomial smoothing support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Haitao Xu Hao Wu Wanjun Zhang Ning Zheng
Copyright Year	2011
Description	Author affiliation: School of Software Engineering, Hangzhou Dianzi University, Hangzhou 310012, China (Haitao Xu; Hao Wu; Wanjun Zhang; Ning Zheng)
Abstract	In China, Hangzhou is the first city to set up the Public Free-Bicycle System. There are many and many technology problems in the decision of intelligent dispatch. Among of these problems, how to list the busy station is very important to design the location of storehouses. Now, there are near 4000 stations in Hangzhou. In this paper, a new data classification method is used to recognize the busy station, which is called Support vector machine (SVM). The original model is a quadratical programming with linear inequalities constraints. In order to get the optimal solution, a new solution method is proposed. The constraints are moved away from the original optimization model by using the approximation solution in the feasible space. Three points under one control parameter smoothing function is used to smoothen the objective function of unconstrained model. It is a sixth order polynomial function. The smoothing performance is investigated. Actually, the busy stations can be recognized from the given data set.
Starting Page	699
Ending Page	704
File Size	606272
Page Count	6
File Format	PDF
ISBN	9781457703058
ISSN	21601348
e-ISBN	9781457703089
DOI	10.1109/ICMLC.2011.6016818
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Logic gates Erbium Smoothing methods Educational institutions BFGS method support vector machine data mining classification smooth function quadratic programming
Content Type	Text
Resource Type	Article

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