Data-Variant Kernel Analysis

Data-Variant Kernel Analysis covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and recent developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and explains 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. It includes information about data formations of offline, distributed, online, cloud, and longitudinal data that is used to classify and predict the future state. 

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.