Data-Variant Kernel Analysis
Yuichi Motai, Virginia Commonwealth University
John Wiley & Sons, Inc., 2015
ISBN: 978-1-119-01932-9;
Language: English
Data-Variant Kernel Analysis:
- Surveys kernel analysis for traditionally developed machine learning techniques such as neural networks (NN), support vector machines (SVM), and principal component analysis (PCA)
- Develops group kernel analysis with 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. In addition, a set of MATLAB code files are included in the appendix.
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