Feature learning and understanding : algorithms and applications

• 作者： Zhao, Haitao, author.
• 其他作者：
  • Lai, Zhihui, author.
  • Leung, Henry, author.
  • Zhang, Xianyi, author.
  • SpringerLink (Online service)
• 其他題名：
  • Information fusion and data science.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Information fusion and data science,
• 主題： Machine learning. , Big data. , Data-driven Science, Modeling and Theory Building. , Machine Learning. , Computational Intelligence. , Pattern Recognition. , Signal, Image and Speech Processing. , Image Processing and Computer Vision.
• ISBN： 9783030407940 (electronic bk.) 、 9783030407933 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 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.
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• 摘要註: 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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