Feature Learning and Understanding〈1st ed. 2020〉 : Algorithms and Applications

個数:1
紙書籍版価格
¥26,274
  • 電子書籍
  • ポイントキャンペーン

Feature Learning and Understanding〈1st ed. 2020〉 : Algorithms and Applications

  • 著者名:Zhao, Haitao/Lai, Zhihui/Leung, Henry/Zhang, Xianyi
  • 価格 ¥21,777 (本体¥19,798)
  • Springer(2020/04/03発売)
  • 3月の締めくくり!Kinoppy 電子書籍・電子洋書 全点ポイント30倍キャンペーン(~3/31)
  • ポイント 5,910pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9783030407933
  • eISBN:9783030407940

ファイル: /

Description

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.


Table of Contents

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.