Data-Variant Kernel Analysis

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John Wiley & Sons, 20 апр. 2015 г. - Всего страниц: 256

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 book is not written in chronological order, allowing the reader to start from any chapter. 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, Support Vector Machines, and Principal Component Analysis
  • Optimizes multiple kernel analysis by selecting principal kernels
  • 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 predictivity 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.

 

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Содержание

Offline Kernel Analysis
41
References
62
Group Kernel Feature Analysis
69
References
92
Dataset 1 Dataset1Dataset2 8 1
98
Kernel combinatory
110
Cloud Kernel Analysis
121
References
147
Equation 3
192
Appendix B Representative Matlab codes
195
where the value 𝛿g denotes the eigenvalues of the database g in Equation 3 14 cor
198
Group Kernel Analysis
201
Online Composite Kernel Analysis
206
Online Data Sequences Contol
208
Alignment Factor
209
Cloud Kernel Analysis
210

Predictive Kernel Analysis
153
Dataset 4
161
References
181
Appendix
189
Plot Computation Time
211
Parallelization
212
121
215
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Об авторе (2015)

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.

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