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.