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Titlebook: Mathematical Analysis and the Mathematics of Computation; Werner R?misch,Thomas Zeugmann Textbook 2016 Springer International Publishing S

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書(shū)目名稱(chēng)Mathematical Analysis and the Mathematics of Computation
編輯Werner R?misch,Thomas Zeugmann
視頻videohttp://file.papertrans.cn/626/625982/625982.mp4
概述Many interdisciplinary applications, including support vector machines, kernel-based learning methods, pattern recognition, statistical learning theory, computer graphics, and approximation theory.Ric
圖書(shū)封面Titlebook: Mathematical Analysis and the Mathematics of Computation;  Werner R?misch,Thomas Zeugmann Textbook 2016 Springer International Publishing S
描述.This book is a comprehensive, unifying introduction to the field of mathematical analysis and the mathematics of computing. It develops the relevant theory at a modern level and it directly relates modern mathematical ideas to their diverse applications...The authors develop the whole theory. Starting with a simple axiom system for the real numbers, they then lay the foundations, developing the theory, exemplifying where it‘s applicable, in turn motivating further development of the theory. They progress from sets, structures, and numbers to metric spaces, continuous functions in metric spaces, linear normed spaces and linear mappings; and then?differential calculus and its applications, the integral calculus, the gamma function, and linear integral operators. They then present important aspects of approximation theory, including numerical integration. The remaining parts of the book are devoted to ordinary differential equations, the discretization of operator equations, and numerical solutions of ordinary differential equations...This textbook contains many exercises of varying degrees of difficulty, suitable for self-study, and at the end of each chapter the authors present mor
出版日期Textbook 2016
關(guān)鍵詞Approximation; Differential Calculus; Integral Calculus; Integral Equations; Interpolation; Mathematical
版次1
doihttps://doi.org/10.1007/978-3-319-42755-3
isbn_softcover978-3-319-82655-4
isbn_ebook978-3-319-42755-3
copyrightSpringer International Publishing Switzerland 2016
The information of publication is updating

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Werner R?misch,Thomas Zeugmanns, including: (1) the ability to yield competitive denoising quality in comparison to specifically trained denoisers in several predetermined noise level and (2) the ability to handle a wide scope of noise levels effectively with a single network. The experimental results reveal its efficiency and e
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Werner R?misch,Thomas Zeugmann has better theoretical interpretability and can be trained more effectively..We use 10-fold cross-validation on Microsoft malware classification challenge dataset to evaluate our model. The results demonstrate that our model can achieve . accuracy with 0.022 log loss. Although our scheme is less pr
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Werner R?misch,Thomas Zeugmannfunction to make sure the learned representations should also be discriminative in label prediction. Furthermore, a structure preservation constraint is imposed to keep local structure consistent during the learning process. Extensive comparison experiments on three widely used datasets demonstrate
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