Data-Variant Kernel Analysis

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John Wiley & Sons, 20 апр. 2015 г. - Всего страниц: 256
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Describes and discusses the variants of kernel analysismethods for data types that have been intensely studied in recentyears

This book covers kernel analysis topics ranging from thefundamental theory of kernel functions to its applications. Thebook surveys the current status, popular trends, and developmentsin kernel analysis studies. The author discusses multiple kernellearning algorithms and how to choose the appropriate kernelsduring the learning phase. Data-Variant Kernel Analysis is anew pattern analysis framework for different types of dataconfigurations. The chapters include data formations of offline,distributed, online, cloud, and longitudinal data, used for kernelanalysis to classify and predict future state. 

Data-Variant Kernel Analysis:

  • Surveys the kernel analysis in the traditionally developedmachine learning techniques, such as Neural Networks (NN), SupportVector Machines (SVM), and Principal Component Analysis (PCA)
  • Develops group kernel analysis with the distributed databasesto compare speed and memory usages
  • Explores the possibility of real-time processes by synthesizingoffline and online databases
  • Applies the assembled databases to compare cloud computingenvironments
  • Examines the prediction of longitudinal data withtime-sequential configurations

Data-Variant Kernel Analysis is a detailed reference forgraduate students as well as electrical and computer engineersinterested in pattern analysis and its application in colon cancerdetection.

Yuichi Motai, Ph.D., is an Associate Professor of Electricaland Computer Engineering at the Virginia Commonwealth University,Richmond, Virginia. He received his Ph.D. with the Robot VisionLaboratory in the School of Electrical and Computer Engineering,Purdue University, West Lafayette, Indiana in 2002.
 

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Содержание

Offline Kernel Analysis
41
References
62
Group Kernel Feature Analysis
69
References
92
between K 6 and the target vectoryy Hence COr11
102
Cloud Kernel Analysis
121
where u V K yy vil Kı K Uill u ui Vºl V VI p Vol
128
References
147
Appendix
189
Appendix B Representative Matlab codes
195
Group Kernel Analysis
201
Online Composite Kernel Analysis
206
Online Data Sequences Contol
208
Alignment Factor
209
Cloud Kernel Analysis
210
Plot Computation Time
211

Predictive Kernel Analysis
153
Up 6Vp 3
159
denotes the eigenvalues of kernel alignment Once we find this optimum
170
References
181
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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