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Data-variant kernel analysis /
"This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications"--
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley,
2015.
|
| Colección: | Wiley series on adaptive and cognitive dynamic systems.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Motai, Yuichi. | |
| 245 | 1 | 0 | |a Data-variant kernel analysis / |c Yuichi Motai, Sensory Intelligence Laboratory, Department of Electrical and Computer Engineering, Virginia Commonwealth University Richmond, VA. |
| 264 | 1 | |a Hoboken, New Jersey : |b Wiley, |c 2015. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Wiley series on adaptive and cognitive dynamic systems | |
| 520 | |a "This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications"-- |c Provided by publisher | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | |a Cover; 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. | |
| 505 | 8 | |a 1.4.1 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). | |
| 505 | 8 | |a 2.2.3 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. | |
| 505 | 8 | |a 3.2 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. | |
| 505 | 8 | |a 3.5.5 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. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Kernel functions. | |
| 650 | 0 | |a Big data |x Mathematics. | |
| 650 | 6 | |a Noyaux (Mathématiques) | |
| 650 | 6 | |a Données volumineuses |x Mathématiques. | |
| 650 | 7 | |a COMPUTERS |x Programming |x Algorithms. |2 bisacsh | |
| 650 | 7 | |a Kernel functions |2 fast | |
| 758 | |i has work: |a Data-variant kernel analysis (Work) |1 https://id.oclc.org/worldcat/entity/E39PCXtJdCTdGvVh3tQbDhVfjd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Motai, Yuichi. |t Data-variant kernel analysis. |d Hoboken : John Wiley & Sons Inc., 2015 |z 9781119019329 |w (DLC) 2015000041 |
| 830 | 0 | |a Wiley series on adaptive and cognitive dynamic systems. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1895925 |z Texto completo |
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