Reducing Computational Requirements Nearest by Kumar Raja (11 results)

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Doctoral Thesis / Dissertation from the year 2018 in the subject Engineering - Computer Engineering, Jawaharlal Nehru University , language: English, abstract: The tremendous growth of data due to the 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.In this work, methods are proposed to overcome the computational requirements of the nearest neighbor classifiers. The work shows some of the possible remedies to overcome problems with nearest neighbor based classifiers. The work proposes a new method of reducing the data set size. The author reduces the data set size in terms of number of samples and also in terms of number of features. Therefore, the holistic goal of the work is to reduce the time and space requirements and at the same time not to degrade the performance of the nearest neighbor classifier.The nearest neighbor classifier is a popular non-parametric classifier used in many fields since the 1950s. It is conceptually a simple classifier and shows good performance.

Taschenbuch. Condition: Neu. Reducing the Computational Requirements of the Nearest Neighbor Classifier | Raja Kumar | Taschenbuch | 144 S. | Englisch | 2019 | GRIN Verlag | EAN 9783346003942 | Verantwortliche Person für die EU: GRIN Publishing GmbH, Waltherstr. 23, 80337 München, info[at]grin[dot]com | Anbieter: preigu.

Taschenbuch. Condition: Neu. Reducing the Computational Requirements of Nearest Neighbor Classifier | R Raja Kumar (u. a.) | Taschenbuch | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200327376 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Doctoral Thesis / Dissertation from the year 2018 in the subject Engineering - Computer Engineering, Jawaharlal Nehru University , language: English, abstract: The tremendous growth of data due to the 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.In this work, methods are proposed to overcome the computational requirements of the nearest neighbor classifiers. The work shows some of the possible remedies to overcome problems with nearest neighbor based classifiers. The work proposes a new method of reducing the data set size. The author reduces the data set size in terms of number of samples and also in terms of number of features. Therefore, the holistic goal of the work is to reduce the time and space requirements and at the same time not to degrade the performance of the nearest neighbor classifier.The nearest neighbor classifier is a popular non-parametric classifier used in many fields since the 1950s. It is conceptually a simple classifier and shows good performance. 144 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 224 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Doctoral Thesis / Dissertation from the year 2018 in the subject Engineering - Computer Engineering, Jawaharlal Nehru University , language: English, abstract: The tremendous growth of data due to the 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. In this work, methods are proposed to overcome the computational requirements of the nearest neighbor classifiers. The work shows some of the possible remedies to overcome problems with nearest neighbor based classifiers. The work proposes a new method of reducing the data set size. The author reduces the data set size in terms of number of samples and also in terms of number of features. Therefore, the holistic goal of the work is to reduce the time and space requirements and at the same time not to degrade the performance of the nearest neighbor classifier. The nearest neighbor classifier is a popular non-parametric classifier used in many fields since the 1950s. It is conceptually a simple classifier and shows good performance.GRIN Publishing GmbH, Waltherstraße 23, 80337 München 144 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kumar R RajaR. Raja Kumar, Phd: Studied Philosophy of Doctorate in Computer Science and Engineering at JNTUA University, Anantapuramu. Associate Professor at RGMCET(Autonomour), India.The tremendous growth of data due to Internet.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 224 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.