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Feature learning and understanding =...

Zhao, Haitao.

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Feature learning and understanding = algorithms and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Feature learning and understanding/ by Haitao Zhao ... [et al.].
其他題名:	algorithms and applications /
其他作者:	Zhao, Haitao.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xiv, 291 p. :ill., digital ;24 cm.
內容註:	Chapter1. A Gentle Introduction to Feature Learning -- Chapter2. Latent Semantic Feature Learning -- Chapter3. Principal Component Analysis -- Chapter4. Local-Geometrical-Structure-based Feature Learning -- Chapter5. Linear Discriminant Analysis -- Chapter6. Kernel-based nonlinear feature learning -- Chapter7. Sparse feature learning -- Chapter8. Low rank feature learning -- Chapter9. Tensor-based Feature Learning -- Chapter10. Neural-network-based Feature Learning: Autoencoder -- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network -- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network.
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-030-40794-0
ISBN:	9783030407940

Feature learning and understanding = algorithms and applications /
Feature learning and understandingalgorithms and applications /[electronic resource] :by Haitao Zhao ... [et al.]. - Cham :Springer International Publishing :2020. - xiv, 291 p. :ill., digital ;24 cm. - Information fusion and data science,2510-1528. - Information fusion and data science..

Chapter1. A Gentle Introduction to Feature Learning -- Chapter2. Latent Semantic Feature Learning -- Chapter3. Principal Component Analysis -- Chapter4. Local-Geometrical-Structure-based Feature Learning -- Chapter5. Linear Discriminant Analysis -- Chapter6. Kernel-based nonlinear feature learning -- Chapter7. Sparse feature learning -- Chapter8. Low rank feature learning -- Chapter9. Tensor-based Feature Learning -- Chapter10. Neural-network-based Feature Learning: Autoencoder -- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network -- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network.

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.

ISBN: 9783030407940

Standard No.: 10.1007/978-3-030-40794-0doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Feature learning and understanding = algorithms and applications /
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505 0 $a Chapter1. A Gentle Introduction to Feature Learning -- Chapter2. Latent Semantic Feature Learning -- Chapter3. Principal Component Analysis -- Chapter4. Local-Geometrical-Structure-based Feature Learning -- Chapter5. Linear Discriminant Analysis -- Chapter6. Kernel-based nonlinear feature learning -- Chapter7. Sparse feature learning -- Chapter8. Low rank feature learning -- Chapter9. Tensor-based Feature Learning -- Chapter10. Neural-network-based Feature Learning: Autoencoder -- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network -- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network.
520 $a 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.
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