{"product_id":"data-variant-kernel-analysis-9781119019329","title":"Data Variant Kernel Analysis","description":"\u003cp\u003e\u003cb\u003eDescribes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years\u003cbr\u003e \u003c\/b\u003e\u003cbr\u003e 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. \u003ci\u003eData-Variant Kernel Analysis\u003c\/i\u003e 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. \u003cbr\u003e \u003cbr\u003e \u003ci\u003eData-Variant Kernel Analysis\u003c\/i\u003e \u003c\/p\u003e \u003cul\u003e \u003cli\u003eSurveys the kernel analysis in the traditionally developed machine learning techniques, such as Neural Networks (NN), Support Vector Machines (SVM), and Principal Component Analysis (PCA)\u003c\/li\u003e \u003cli\u003eDevelops group kernel analysis with the distributed databases to compare speed and memory usages\u003c\/li\u003e \u003cli\u003eExplores the possibility of real-time processes by synthesizing offline and online databases\u003c\/li\u003e \u003cli\u003eApplies the assembled databases to compare cloud computing environments\u003c\/li\u003e \u003cli\u003eExamines the prediction of longitudinal data with time-sequential configurations\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData-Variant Kernel Analysis\u003c\/i\u003e 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.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Yuichi Motai\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Wiley\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 04\/20\/2015\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 256\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.25lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.40h x 6.30w x 0.90d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781119019329\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eYUICHI MOTAI, Ph.D.\u003c\/b\u003e, is an Associate Professor of Electrical and Computer Engineering at the Virginia Commonwealth University, Richmond, Virginia. He received his Ph.D. with the Robot Vision Laboratory in the School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana in 2002.\u003cbr\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Hardcover","offer_id":44040694497395,"sku":"9.78112E+12","price":220.3,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/files\/img_4cbaf973-164d-4483-b147-7f6b52ad2f61.jpg?v=1761741481","url":"https:\/\/bookstorenmore.com\/products\/data-variant-kernel-analysis-9781119019329","provider":"Bookstore N More","version":"1.0","type":"link"}