Feature Learning and Understanding - Algorithms and Applications (Hardcover, 1st ed. 2020)

This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Information Fusion and Data Science
Release date:	April 2020
First published:	2020
Authors:	Haitao Zhao • Zhihui Lai • Henry Leung • Xianyi Zhang
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	291
Edition:	1st ed. 2020
ISBN-13:	978-3-03-040793-3
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Computing & IT > Applications of computing > Image processing > General Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General
LSN:	3-03-040793-4
Barcode:	9783030407933

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