LEADER 04987nam 2200685 450 001 9910812500603321 005 20230807210258.0 010 $a1-119-01934-6 010 $a1-119-01935-4 035 $a(CKB)2670000000610011 035 $a(EBL)1895925 035 $a(SSID)ssj0001458410 035 $a(PQKBManifestationID)12614490 035 $a(PQKBTitleCode)TC0001458410 035 $a(PQKBWorkID)11444233 035 $a(PQKB)11689081 035 $a(MiAaPQ)EBC1895925 035 $a(DLC) 2015007727 035 $a(Au-PeEL)EBL1895925 035 $a(CaPaEBR)ebr11048136 035 $a(CaONFJC)MIL770220 035 $a(OCoLC)904047560 035 $a(EXLCZ)992670000000610011 100 $a20150511h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData-variant kernel analysis /$fYuichi Motai 210 1$aHoboken, New Jersey :$cWiley,$d2015. 210 4$dİ2015 215 $a1 online resource (248 p.) 225 1 $aWiley Series on Adaptive and Cognitive Dynamic Systems 300 $aDescription based upon print version of record. 311 $a1-119-01933-8 311 $a1-119-01932-X 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; Title Page; Copyright; Contents; List of Figures; List of Tables; Preface; Acknowledgments; Chapter 1 Survey; 1.1 Introduction of Kernel Analysis; 1.2 Kernel Offline Learning; 1.2.1 Choose the Appropriate Kernels; 1.2.2 Adopt KA into the Traditionally Developed Machine Learning Techniques; 1.2.3 Structured Database with Kernel; 1.3 Distributed Database with Kernel; 1.3.1 Multiple Database Representation; 1.3.2 Kernel Selections Among Heterogeneous Multiple Databases; 1.3.3 Multiple Database Representation KA Applications to Distributed Databases; 1.4 Kernel Online Learning 327 $a1.4.1 Kernel-Based Online Learning Algorithms1.4.2 Adopt ""Online"" KA Framework into the Traditionally Developed Machine Learning Techniques; 1.4.3 Relationship Between Online Learning and Prediction Techniques; 1.5 Prediction with Kernels; 1.5.1 Linear Prediction; 1.5.2 Kalman Filter; 1.5.3 Finite-State Model; 1.5.4 Autoregressive Moving Average Model; 1.5.5 Comparison of Four Models; 1.6 Future Direction and Conclusion; References; Chapter 2 Offline Kernel Analysis; 2.1 Introduction; 2.2 Kernel Feature Analysis; 2.2.1 Kernel Basics; 2.2.2 Kernel Principal Component Analysis (KPCA) 327 $a2.2.3 Accelerated Kernel Feature Analysis (AKFA)2.2.4 Comparison of the Relevant Kernel Methods; 2.3 Principal Composite Kernel Feature Analysis (PC-KFA); 2.3.1 Kernel Selections; 2.3.2 Kernel Combinatory Optimization; 2.4 Experimental Analysis; 2.4.1 Cancer Image Datasets; 2.4.2 Kernel Selection; 2.4.3 Kernel Combination and Reconstruction; 2.4.4 Kernel Combination and Classification; 2.4.5 Comparisons of Other Composite Kernel Learning Studies; 2.4.6 Computation Time; 2.5 Conclusion; References; Chapter 3 Group Kernel Feature Analysis; 3.1 Introduction 327 $a3.2 Kernel Principal Component Analysis (KPCA)3.3 Kernel Feature Analysis (KFA) for Distributed Databases; 3.3.1 Extract Data-Dependent Kernels Using KFA; 3.3.2 Decomposition of Database Through Data Association via Recursively Updating Kernel Matrices; 3.4 Group Kernel Feature Analysis (GKFA); 3.4.1 Composite Kernel: Kernel Combinatory Optimization; 3.4.2 Multiple Databases Using Composite Kernel; 3.5 Experimental Results; 3.5.1 Cancer Databases; 3.5.2 Optimal Selection of Data-Dependent Kernels; 3.5.3 Kernel Combinatory Optimization; 3.5.4 Composite Kernel for Multiple Databases 327 $a3.5.5 K-NN Classification Evaluation with ROC3.5.6 Comparison of Results with Other Studies on Colonography; 3.5.7 Computational Speed and Scalability Evaluation of GKFA; 3.6 Conclusions; References; Chapter 4 Online Kernel Analysis; 4.1 Introduction; 4.2 Kernel Basics: A Brief Review; 4.2.1 Kernel Principal Component Analysis; 4.2.2 Kernel Selection; 4.3 Kernel Adaptation Analysis of PC-KFA; 4.4 Heterogeneous vs. Homogeneous Data for Online PC-KFA; 4.4.1 Updating the Gram Matrix of the Online Data; 4.4.2 Composite Kernel for Online Data 327 $a4.5 Long-Term Sequential Trajectories with Self-Monitoring 330 $a"This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications"--$cProvided by publisher. 410 0$aWiley series on adaptive and cognitive dynamic systems. 606 $aKernel functions 606 $aBig data$xMathematics 615 0$aKernel functions. 615 0$aBig data$xMathematics. 676 $a515/.9 686 $aCOM051300$2bisacsh 700 $aMotai$b Yuichi$01695258 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910812500603321 996 $aData-variant kernel analysis$94074376 997 $aUNINA