Angielski
Describes and discusses the variants of kernel analysismethods for data types that have been intensely studied in recentyears This book covers kernel analysis topics ranging from thefundamental theory of kernel functions to its applications. Thebook surveys the current status, popular trends, and developmentsin kernel analysis studies. The author discusses multiple kernellearning algorithms and how to choose the appropriate kernelsduring the learning phase. Data-Variant Kernel Analysis is anew pattern analysis framework for different types of dataconfigurations. The chapters include data formations of offline,distributed, online, cloud, and longitudinal data, used for kernelanalysis to classify and predict future state. Data-Variant Kernel Analysis: * Surveys the kernel analysis in the traditionally developedmachine learning techniques, such as Neural Networks (NN), SupportVector Machines (SVM), and Principal Component Analysis (PCA) * Develops group kernel analysis with the distributed databasesto compare speed and memory usages * Explores the possibility of real-time processes by synthesizingoffline and online databases * Applies the assembled databases to compare cloud computingenvironments * Examines the prediction of longitudinal data withtime-sequential configurations Data-Variant Kernel Analysis is a detailed reference forgraduate students as well as electrical and computer engineersinterested in pattern analysis and its application in colon cancerdetection.
