Okutmutur, Baver  | LAP Lambert Academic

111,750원  | 20130122  | 9783838356310

This is a series of lectures we have held during the academic year 2004-2005 at the Department of Mathematics of the Bilkent University in the seminar of operator theory.

 | Now Publishers

167,620원  | 20151120  | 9781680830927

Hilbert space theory is an invaluable mathematical tool in numerous signal processing and systems theory applications. Hilbert spaces satisfying certain additional properties are known as Reproducing Kernel Hilbert Spaces (RKHSs). This primer gives a gentle and novel introduction to RKHS theory. It also presents several classical applications. It concludes by focusing on recent developments in the machine learning literature concerning embeddings of random variables.

Daniel Alpay  | American Mathematical Society

97,740원  | 20210101  | 9780821821558

The generalized Schur functions have important applications to the theory of linear operators, to signal processing and control theory, and to other areas of engineering. This book looks at matrix-valued Schur functions and their applications from the unifying point of view of spaces with reproducing kernels.

 | Springer Nature B.V.

74,480원  | 20220518  | 9783030983178

This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel?Hilbert Spaces (RKHS), accompanied with tools from regularization theory, can be effectively used in the design and justification of kernel learning algorithms, which can address problems in several areas of artificial intelligence.

 | KS OmniScriptum Publishing

180,660원  | 20090908  | 9783838313085

Over the last few decades, there has been much research on reproducing kernel Hilbert space (RKHS) for machine learning. This monograph applies RKHS for nonlinear signal processing. It proposes a new statistical descriptor, called correntropy, to characterize the higher order statistical information and nonlinearity intrinsic to random processes.

 | Kluwer

465,600원  | 20080325  | 9781402076794

Since the first papers laying its foundations as a subfield ofComplex Analysis the theory of reproducing kernel Hilbert spaces(RKHS) has proved to be a powerful tool in many fields of Pure andApplied Mathematics, and in particular in Probability a...

Vern I. Paulsen  | Cambridge University Press

169,480원  | 20170809  | 9781107104099

Covering the fundamental underlying theory as well as a range of applications, this unique text provides a unified overview of reproducing kernel Hilbert spaces. It offers an unrivalled and accessible introduction to the field, ideal for graduate students and researchers working in functional analysis or its applications.