Data-Variant Kernel Analysis

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 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 (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.

Keywords: Kernel Analysis, Heterogeneous Data, Homogenous Data, Support Vector Machine (SVM), One-Class Support SVMs, Diffusion Kernels, Computed Tomography, Group Kernel Feature Analysis, Kalman Filter, Karhunen–Loève Transform, Kernel Multivariate Analysis, Multiple Kernel Learning, Kernel Fisher Discriminant Analysis, Pattern Analysis, Pattern Analysis