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ÏëÖªµÀÊÇ·ñ±»¼ìË÷£¬ÀͼݳæÓѲéÔÄ£ºThe Spatial Classification Algorithm of K-Nearest Neighbor Based on Spatial Predicate
Ma Yu, Gao Yuling, Song Shaoyun
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Accession number:       
20132916505588
        Title:        The spatial classification algorithm of K-nearest neighbor based on spatial predicate
        Authors:         Ma, Yu1 ; Gao, Yu Ling2 ; Song, Shao Yun1
        Author affiliation:        1 School of Information Technology and Engineering, Yuxi Normal University, Yuxi, Yunnan, China
                2 School of Foreign Languages, Yuxi Normal University, Yuxi, Yunnan, China
        Source title:        Advanced Materials Research
        Abbreviated source title:        Adv. Mater. Res.
        Volume:        706-708
        Monograph title:        Mechatronics and Intelligent Materials III
        Issue date:        2013
        Publication year:        2013
        Pages:        1928-1931
        Language:        English
        ISSN:         10226680
        ISBN-13:         9783037857106
        Document type:        Conference article (CA)
        Conference name:        2013 3rd International Conference on Mechatronics and Intelligent Materials, MIM 2013
        Conference date:        May 18, 2013 - May 19, 2013
        Conference location:        XiShuangBanNa, China
        Conference code:         97695
        Sponsor:        Hong Kong Control Engin. and Inform.; Science Research Assoc. (CEIS); Internat. Frontiers of science and; technol. Research Assoc. (IFST); Integrated Research Center for Green Living Techniques; National Chin-Yi University of Technology
        Publisher:        Trans Tech Publications Ltd, Kreuzstrasse 10, Zurich-Durnten, CH-8635, Switzerland
        Abstract:        Traditional k-Nearest Neighbor Algorithm (short for KNN) is usually used in the spatial classification; however, the problem of low-speed searching exists in this method. In order to avoid this kind of disadvantage, this paper puts forward a new spatial classification algorithm of K-nearest neighbor based on spatial predicate. This method searches the object set which is similar to the test object in spatial concept and uses spatial predicate to help search the object set, which narrows the searching range and reduces the operating time of KNN algorithm. © (2013) Trans Tech Publications, Switzerland.
        Number of references:        7
        Main heading:         Learning algorithms
        Controlled terms:         Intelligent materials  -  Pattern recognition
        Uncontrolled terms:         K nearest neighbor algorithm  -  K-nearest neighbors  -  k-NN algorithm  -  Operating time  -  Spatial classification  -  Spatial concepts  -  Spatial predicates  -  Test object
        Classification code:         415 Metals, Plastics, Wood and Other Structural Materials -  716 Telecommunication; Radar, Radio and Television -  723 Computer Software, Data Handling and Applications
        DOI:        10.4028/www.scientific.net/AMR.706-708.1928
        Database:        Compendex
                Compilation and indexing terms, © 2012 Elsevier Inc.
2Â¥2013-07-28 09:05:42
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