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.
