Reducing the Computational Requirements of Nearest Neighbor Classifier.

      Аннотация: The tremendous growth of data due to Internet and electronic commerce has created serious challenges to the researches in pattern recognition. There is a need of processing and analysing data. Advances in data mining and knowledge discovery provide the requirement of new approaches to reduce the data. The reduction of data is an important problem that attracts the eyes of researches in pattern recognition. The reduction of data is the core problem in classifiers especially for Nearest Neighbor Classifier since it stores the entire training set for classifying the query patterns and also the classifier needs to compute the distances between the query pattern and each and every pattern from the stored training set. Hence the time and space requirements are high for Nearest Neighbor Classifier. In this book, methods are proposed to overcome the computational requirements of Nearest Neighbor Classifier. This book explores some of the possible remedies to overcome the problems with Nearest Neighbor based classifiers. The main disadvantages of Nearest Neighbor Classifier can be avoided using the proposed methods in this book.

Ключевые слова: Nearest Neighbor Classifier, Pattern recognition, OCNN, Discriminant Analysis, Feature Selection