Synopsis
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.
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Reducing the Computational Requirements of the Nearest Neighbor Classifier
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783346003942
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Reducing the Computational Requirements of the Nearest Neighbor Classifier
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GRIN Verlag, GRIN Verlag Okt 2019, 2019
ISBN 10 : 3346003949
ISBN 13 : 9783346003942
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783346003942
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Taschenbuch. Etat : 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. N° de réf. du vendeur 9783346003942
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Reducing the Computational Requirements of the Nearest Neighbor Classifier
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Taschenbuch. Etat : 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. N° de réf. du vendeur 117541640
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