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Data-Variant Kernel Analysis.

By: Motai, Yuichi.
Contributor(s): Bogucka, Hanna | Wiley.
Material type: TextTextSeries: eBooks on Demand.Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control: Publisher: Somerset : Wiley, 2015Description: 1 online resource (248 p.).ISBN: 9781119019336.Subject(s): Big data -- Mathematics | Kernel functionsGenre/Form: Electronic books.Additional physical formats: Print version:: Data-Variant Kernel AnalysisDDC classification: 515/.9 LOC classification: QA353.K47 -- .M68 2015ebOnline resources: Click here to view this ebook.
Contents:
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 -- 1.4.1 Kernel-Based Online Learning Algorithms -- 1.4.2 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) -- 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 -- 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 -- 3.5.5 K-NN Classification Evaluation with ROC -- 3.5.6 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
Summary: Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurat
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Electronic Book UT Tyler Online
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QA353 .K47 M68 2015 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1895925 Available EBL1895925

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 -- 1.4.1 Kernel-Based Online Learning Algorithms -- 1.4.2 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) -- 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 -- 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 -- 3.5.5 K-NN Classification Evaluation with ROC -- 3.5.6 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

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications. The book surveys the current status, popular trends, and developments in kernel analysis studies. The author discusses multiple kernel learning algorithms and how to choose the appropriate kernels during the learning phase. Data-Variant Kernel Analysis is a new pattern analysis framework for different types of data configurat

Description based upon print version of record.

Author notes provided by Syndetics

<p> YUICHI MOTAI, Ph.D. , is an Associate Professor of Electrical and Computer Engineering at the Virginia Commonwealth University, Richmond, Virginia. He received his Ph.D. with the Robot Vision Laboratory in the School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana in 2002.</p>

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