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.

描述并讨论了近年来被密集研究的数据类型的内核分析方法的变种。本书涵盖了从核函数的基本理论到其应用的核分析主题。本书调查了核分析研究的现状、流行趋势和发展。作者讨论了多种核学习算法以及如何在学习阶段选择合适的核。数据变异核分析是一个新的模式分析框架，适用于不同类型的数据配置。这几章包括离线、分布式、在线、云和纵向数据的数据形态，用于核分析分类和预测未来状态。