Improvements to Nearest Neighbor Classifier: Pattern Synthesis, Compact Data Representation and other Schemes - Softcover
Synopsis
Nearest neighbor classifier (NNC), a non-parametric pattern classification technique is not only simple to use, but often shows good performance. It is used in several domains, like data mining, machine learning, image/video/audio data analysis and retrieval, etc. It has some shortcomings or limitations, like it can be biased due to curse of dimensionality effect, huge computational requirements, etc. With large number of training instances it can achieve a better classification accuracy. But, this can worse the computational burden. The monograph presents a series of techniques whereby number of training instances can be artificially increased and can be stored in compact data representation schemes. This means, training set size can be virtually increased, but computational burden is not. The monograph presents, mainly, pattern synthesis techniques called partition based pattern synthesis and overlap based pattern synthesis, and their respective compact data structures. It is shown, both theoretically and experimentally that the proposed methods are effective. The monograph also presents some other improvements to NNC and presents a constant time NNC also.
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About the Author
P. Viswanth received his M.Tech from IIT-Madras, and Ph.D.(best thesis award given) from IISc-Bangalore. He worked at IIT-Guwahati from 2005 to 2008. Since then he is working as a Professor and Dean, R&D (Electrical Sciences)at Rajeev Gandhi Memorial College of Eng. & Tech., Nandyal,A.P, India. URL:https://sites.google.com/site/viswanathpulabaigari
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Improvements to Nearest Neighbor Classifier
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Nearest neighbor classifier (NNC), a non-parametric pattern classification technique is not only simple to use, but often shows good performance. It is used in several domains, like data mining, machine learning, image/video/audio data analysis and retrieval, etc. It has some shortcomings or limitations, like it can be biased due to curse of dimensionality effect, huge computational requirements, etc. With large number of training instances it can achieve a better classification accuracy. But, this can worse the computational burden. The monograph presents a series of techniques whereby number of training instances can be artificially increased and can be stored in compact data representation schemes. This means, training set size can be virtually increased, but computational burden is not. The monograph presents, mainly, pattern synthesis techniques called partition based pattern synthesis and overlap based pattern synthesis, and their respective compact data structures. It is shown, both theoretically and experimentally that the proposed methods are effective. The monograph also presents some other improvements to NNC and presents a constant time NNC also. 184 pp. Englisch. Seller Inventory # 9783845416496
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Improvements to Nearest Neighbor Classifier
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Pulabaigari ViswanathP. Viswanth received his M.Tech from IIT-Madras, and Ph.D.(best thesis award given) from IISc-Bangalore. He worked at IIT-Guwahati from 2005 to 2008. Since then he is working as a Professor and Dean, R&D (Electri. Seller Inventory # 5481410
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Improvements to Nearest Neighbor Classifier | Pattern Synthesis, Compact Data Representation and other Schemes
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Taschenbuch. Condition: Neu. Improvements to Nearest Neighbor Classifier | Pattern Synthesis, Compact Data Representation and other Schemes | Viswanath Pulabaigari (u. a.) | Taschenbuch | 184 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783845416496 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106843616
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Improvements to Nearest Neighbor Classifier
Published by
LAP LAMBERT Academic Publishing Aug 2011, 2011
ISBN 10: 3845416491
ISBN 13: 9783845416496
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Nearest neighbor classifier (NNC), a non-parametric pattern classification technique is not only simple to use, but often shows good performance. It is used in several domains, like data mining, machine learning, image/video/audio data analysis and retrieval, etc. It has some shortcomings or limitations, like it can be biased due to curse of dimensionality effect, huge computational requirements, etc. With large number of training instances it can achieve a better classification accuracy. But, this can worse the computational burden. The monograph presents a series of techniques whereby number of training instances can be artificially increased and can be stored in compact data representation schemes. This means, training set size can be virtually increased, but computational burden is not. The monograph presents, mainly, pattern synthesis techniques called partition based pattern synthesis and overlap based pattern synthesis, and their respective compact data structures. It is shown, both theoretically and experimentally that the proposed methods are effective. The monograph also presents some other improvements to NNC and presents a constant time NNC also.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 184 pp. Englisch. Seller Inventory # 9783845416496
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Improvements to Nearest Neighbor Classifier : Pattern Synthesis, Compact Data Representation and other Schemes
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ISBN 10: 3845416491
ISBN 13: 9783845416496
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Nearest neighbor classifier (NNC), a non-parametric pattern classification technique is not only simple to use, but often shows good performance. It is used in several domains, like data mining, machine learning, image/video/audio data analysis and retrieval, etc. It has some shortcomings or limitations, like it can be biased due to curse of dimensionality effect, huge computational requirements, etc. With large number of training instances it can achieve a better classification accuracy. But, this can worse the computational burden. The monograph presents a series of techniques whereby number of training instances can be artificially increased and can be stored in compact data representation schemes. This means, training set size can be virtually increased, but computational burden is not. The monograph presents, mainly, pattern synthesis techniques called partition based pattern synthesis and overlap based pattern synthesis, and their respective compact data structures. It is shown, both theoretically and experimentally that the proposed methods are effective. The monograph also presents some other improvements to NNC and presents a constant time NNC also. Seller Inventory # 9783845416496
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