Articles liés à Data-Variant Kernel Analysis
Synopsis
Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years
This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state.
Data-Variant Kernel Analysis
- Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA)
- Develops group kernel analysis with the distributed databases to compare speed and memory usages
- Explores the possibility of real-time processes by synthesizing offline and online databases
- Applies the assembled databases to compare cloud computing environments
- Examines the prediction of longitudinal data with time-sequential configurations
Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
YUICHI MOTAI, Ph.D., is an Associate Professor of Electrical and Computer Engineering at the Virginia Commonwealth University, Richmond, Virginia. He received his Ph.D. with the Robot Vision Laboratory in the School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana in 2002.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Data-Variant Kernel Analysis
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John Wiley & Sons, 2015
ISBN 10 : 111901932X
ISBN 13 : 9781119019329
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Data-Variant Kernel Analysis
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Data-Variant Kernel Analysis
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Data-Variant Kernel Analysis
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ISBN 13 : 9781119019329
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Data-Variant Kernel Analysis (Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control)
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Data Variant Kernel Analysis
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Buch. Etat : Neu. Neuware - Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years. N° de réf. du vendeur 9781119019329
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Data-Variant Kernel Analysis
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John Wiley & Sons Inc, 2015
ISBN 10 : 111901932X
ISBN 13 : 9781119019329
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Etat : New. Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. Series: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control. Num Pages: 256 pages. BIC Classification: TJK. Category: (P) Professional & Vocational. Dimension: 241 x 163 x 22. Weight in Grams: 378. . 2015. 1st Edition. Hardcover. . . . . N° de réf. du vendeur V9781119019329
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Data-Variant Kernel Analysis (Hardcover)
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John Wiley & Sons Inc, New York, 2015
ISBN 10 : 111901932X
ISBN 13 : 9781119019329
Neuf
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Edition originale
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Hardcover. Etat : new. Hardcover. Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurations. The chapters include data formations of offline, distributed, online, cloud, and longitudinal data, used for kernel analysis to classify and predict future state. Data-Variant Kernel Analysis: Surveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA)Develops group kernel analysis with the distributed databases to compare speed and memory usagesExplores the possibility of real-time processes by synthesizing offline and online databasesApplies the assembled databases to compare cloud computing environmentsExamines the prediction of longitudinal data with time-sequential configurations Data-Variant Kernel Analysis is a detailed reference for graduate students as well as electrical and computer engineers interested in pattern analysis and its application in colon cancer detection. Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781119019329
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