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In many real-world classification problems the local structure is more important than the global structure and in the dimensionality reduction algorithms such as principle component analysis (PCA) it preserves the global structure of the dataset and ignores the local structure of the dataset, therefore this book introduce the Locality Preserving Projections (LPP) algorithm that is preserving the local structure of the datasets. LPP is a linear projective maps that arise by solving variational problem that optimally preserves the neighborhood structure of the data set. The aims of this book are to compare between PCA and LPP in terms of accuracy, develop appropriate representations of complex data by reducing the dimensions of the data and explain the importance of using LPP with logistic regression. The methodology of this book compared the proposed LPP approach with PCA method on five different data sets using dimensionality reduction toolbox (drtoolbox) in matlab software and evaluation the model using cross validation method and then calculated the performance measures(accuracy, sensitivity, Specificity , precision, f-score and roc curve) of both.
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Azza Kamal Ahmed ,Master of Computer Science at University of Gezira, (2015). Studied Statistics/Computer at Gezira University , Faculty of Mathematical and Computer Sciences, (2009). Web developer at Informatics Administration , University of Gezira.
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A Logistic Regression Analysis of LPP and PCA on Classification
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In many real-world classification problems the local structure is more important than the global structure and in the dimensionality reduction algorithms such as principle component analysis (PCA) it preserves the global structure of the dataset and ignores the local structure of the dataset, therefore this book introduce the Locality Preserving Projections (LPP) algorithm that is preserving the local structure of the datasets. LPP is a linear projective maps that arise by solving variational problem that optimally preserves the neighborhood structure of the data set. The aims of this book are to compare between PCA and LPP in terms of accuracy, develop appropriate representations of complex data by reducing the dimensions of the data and explain the importance of using LPP with logistic regression. The methodology of this book compared the proposed LPP approach with PCA method on five different data sets using dimensionality reduction toolbox (drtoolbox) in matlab software and evaluation the model using cross validation method and then calculated the performance measures(accuracy, sensitivity, Specificity , precision, f-score and roc curve) of both. 68 pp. Englisch. Bestandsnummer des Verkäufers 9783330802742
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A Logistic Regression Analysis of LPP and PCA on Classification
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kamal AzzaAzza Kamal Ahmed ,Master of Computer Science at University of Gezira, (2015). Studied Statistics/Computer at Gezira University , Faculty of Mathematical and Computer Sciences, (2009). Web developer at Informatics Administr. Bestandsnummer des Verkäufers 151242770
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A Logistic Regression Analysis of LPP and PCA on Classification
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A Logistic Regression Analysis of LPP and PCA on Classification
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In many real-world classification problems the local structure is more important than the global structure and in the dimensionality reduction algorithms such as principle component analysis (PCA) it preserves the global structure of the dataset and ignores the local structure of the dataset, therefore this book introduce the Locality Preserving Projections (LPP) algorithm that is preserving the local structure of the datasets. LPP is a linear projective maps that arise by solving variational problem that optimally preserves the neighborhood structure of the data set. The aims of this book are to compare between PCA and LPP in terms of accuracy, develop appropriate representations of complex data by reducing the dimensions of the data and explain the importance of using LPP with logistic regression. The methodology of this book compared the proposed LPP approach with PCA method on five different data sets using dimensionality reduction toolbox (drtoolbox) in matlab software and evaluation the model using cross validation method and then calculated the performance measures(accuracy, sensitivity, Specificity , precision, f-score and roc curve) of both.Books on Demand GmbH, Überseering 33, 22297 Hamburg 68 pp. Englisch. Bestandsnummer des Verkäufers 9783330802742
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In many real-world classification problems the local structure is more important than the global structure and in the dimensionality reduction algorithms such as principle component analysis (PCA) it preserves the global structure of the dataset and ignores the local structure of the dataset, therefore this book introduce the Locality Preserving Projections (LPP) algorithm that is preserving the local structure of the datasets. LPP is a linear projective maps that arise by solving variational problem that optimally preserves the neighborhood structure of the data set. The aims of this book are to compare between PCA and LPP in terms of accuracy, develop appropriate representations of complex data by reducing the dimensions of the data and explain the importance of using LPP with logistic regression. The methodology of this book compared the proposed LPP approach with PCA method on five different data sets using dimensionality reduction toolbox (drtoolbox) in matlab software and evaluation the model using cross validation method and then calculated the performance measures(accuracy, sensitivity, Specificity , precision, f-score and roc curve) of both. Bestandsnummer des Verkäufers 9783330802742
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Taschenbuch. Zustand: Neu. A Logistic Regression Analysis of LPP and PCA on Classification | Azza Kamal | Taschenbuch | 68 S. | Englisch | 2016 | Noor Publishing | EAN 9783330802742 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 107925498
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