LEADER 04965nam 22008175 450 001 9910741167703321 005 20200705095718.0 010 $a3-030-40794-2 024 7 $a10.1007/978-3-030-40794-0 035 $a(CKB)4100000010858861 035 $a(DE-He213)978-3-030-40794-0 035 $a(MiAaPQ)EBC6157459 035 $a(PPN)243762763 035 $a(EXLCZ)994100000010858861 100 $a20200403d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFeature Learning and Understanding $eAlgorithms and Applications /$fby Haitao Zhao, Zhihui Lai, Henry Leung, Xianyi Zhang 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XIV, 291 p. 126 illus., 109 illus. in color.) 225 1 $aInformation Fusion and Data Science,$x2510-1528 311 $a3-030-40793-4 327 $aChapter1. 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. 330 $aThis book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence. 410 0$aInformation Fusion and Data Science,$x2510-1528 606 $aSociophysics 606 $aEconophysics 606 $aMachine learning 606 $aComputational intelligence 606 $aPattern recognition 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aOptical data processing 606 $aData-driven Science, Modeling and Theory Building$3https://scigraph.springernature.com/ontologies/product-market-codes/P33030 606 $aMachine Learning$3https://scigraph.springernature.com/ontologies/product-market-codes/I21010 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 615 0$aSociophysics. 615 0$aEconophysics. 615 0$aMachine learning. 615 0$aComputational intelligence. 615 0$aPattern recognition. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aOptical data processing. 615 14$aData-driven Science, Modeling and Theory Building. 615 24$aMachine Learning. 615 24$aComputational Intelligence. 615 24$aPattern Recognition. 615 24$aSignal, Image and Speech Processing. 615 24$aImage Processing and Computer Vision. 676 $a006.31 700 $aZhao$b Haitao$4aut$4http://id.loc.gov/vocabulary/relators/aut$0935380 702 $aLai$b Zhihui$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aLeung$b Henry$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aZhang$b Xianyi$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910741167703321 996 $aFeature Learning and Understanding$92106932 997 $aUNINA