Kernel Methods : Pattern recognition, Principal component analysis

Language: English

Published by Omniscriptum, 2026

613731653X / 9786137316535

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nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. In computerscience, kernel methods (KMs) are a class of algorithms for patternanalysis, whose best known element is the support vector machine (SVM).The general task of pattern analysis is to find and study general typesof relations (for example clusters, rankings, principal componentscorrelations, classifications) in general types of data (such assequences, text documents, sets of points, vectors, images, etc.).KMsapproach the problem by mapping the data into a high dimensional featurespace, where each coordinate corresponds to one feature of the dataitems, transforming the data into a set of points in a Euclidean space.In that space, a variety of methods can be used to find relations in thedata. Since the mapping can be quite general (not necessarily linearfor example), the relations found in this way are accordingly verygeneral. This approach is called the kernel trick.…

Seller Inventory # 9786137316535

Title
Kernel Methods : Pattern recognition, Principal component analysis
Author
Kristen Nehemiah Horst
Publisher
Omniscriptum
Publication year
2026
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
613731653X
ISBN 13
9786137316535
Item weight
125 grams
Dimensions
220x150x5 mm

AHA-BUCH GmbH

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