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Record Nr. |
UNINA9910741167703321 |
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Autore |
Zhao Haitao |
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Titolo |
Feature Learning and Understanding : Algorithms and Applications / / by Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XIV, 291 p. 126 illus., 109 illus. in color.) |
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Collana |
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Information Fusion and Data Science, , 2510-1528 |
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Disciplina |
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Soggetti |
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Sociophysics |
Econophysics |
Machine learning |
Computational intelligence |
Pattern recognition |
Signal processing |
Image processing |
Speech processing systems |
Optical data processing |
Data-driven Science, Modeling and Theory Building |
Machine Learning |
Computational Intelligence |
Pattern Recognition |
Signal, Image and Speech Processing |
Image Processing and Computer Vision |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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Learning: Autoencoder -- Chapter11. Neural-network-based Feature Learning: Convolutional Neural Network -- Chapter12. Neural-network-based Feature Learning: Recurrent Neural Network. |
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Sommario/riassunto |
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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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