
Improvements to Nearest Neighbor Classifier
Pattern Synthesis, Compact Data Representation and other Schemes
LAP Lambert Academic Publishing
Published on 12. August 2011
Book
Paperback/Softback
184 pages
978-3-8454-1649-6 (ISBN)
Description
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.
More details
Language
English
Place of publication
Germany
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 220 mm
Width: 150 mm
Thickness: 12 mm
Weight
292 gr
ISBN-13
978-3-8454-1649-6 (9783845416496)
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Schweitzer Classification
Persons
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