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" Data-variant kernel analysis / "
Yuichi Motai, Sensory Intelligence Laboratory, Department of Electrical and Computer Engineering, Virginia Commonwealth University Richmond, VA
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BL
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| Record Number
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641322
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dltt
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| Main Entry
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Motai, Yuichi.
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| Title & Author
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Data-variant kernel analysis /\ Yuichi Motai, Sensory Intelligence Laboratory, Department of Electrical and Computer Engineering, Virginia Commonwealth University Richmond, VA
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| Series Statement
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Wiley series on adaptive and cognitive dynamic systems
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| Page. NO
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1 online resource
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| ISBN
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9781119019343
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: 1119019346
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: 9781119019336
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: 1119019338
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: 9781119019350
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: 1119019354
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111901932X
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9781119019329
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| Bibliographies/Indexes
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Includes bibliographical references and index
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| Contents
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Survey -- Introduction of Kernel Analysis -- Kernel Offline Learning -- Choose the Appropriate Kernels -- Adopt KA into the Traditionally Developed Machine Learning Techniques -- Structured Database with Kernel -- Distributed Database with Kernel -- Multiple Database Representation -- Kernel Selections Among Heterogeneous Multiple Databases -- Multiple Database Representation KA Applications to Distributed Databases -- Kernel Online Learning -- Kernel-Based Online Learning Algorithms -- Adopt "Online" KA Framework into the Traditionally Developed Machine Learning Techniques -- Relationship Between Online Learning and Prediction Techniques -- Prediction with Kernels -- Linear Prediction -- Kalman Filter -- Finite-State Model -- Autoregressive Moving Average Model -- Comparison of Four Models -- Future Direction and Conclusion -- References -- Offline Kernel Analysis -- Introduction -- Kernel Feature Analysis -- Kernel Basics -- Kernel Principal Component Analysis (KPCA) -- Accelerated Kernel Feature Analysis (AKFA) -- Comparison of the Relevant Kernel Methods -- Principal Composite Kernel Feature Analysis (PC-KFA) -- Kernel Selections -- Kernel Combinatory Optimization -- Experimental Analysis -- Cancer Image Datasets -- Kernel Selection -- Kernel Combination and Reconstruction -- Kernel Combination and Classification -- Comparisons of Other Composite Kernel Learning Studies -- Computation Time -- Conclusion -- References -- Group Kernel Feature Analysis -- Introduction -- Kernel Principal Component Analysis (KPCA) -- Kernel Feature Analysis (KFA) for Distributed Databases -- Extract Data-Dependent Kernels Using KFA -- Decomposition of Database Through Data Association via Recursively Updating Kernel Matrices -- Group Kernel Feature Analysis (GKFA) -- Composite Kernel: Kernel Combinatory Optimization -- Multiple Databases Using Composite Kernel -- Experimental Results -- Cancer Databases -- Optimal Selection of Data-Dependent Kernels -- Kernel Combinatory Optimization -- Composite Kernel for Multiple Databases -- K-NN Classification Evaluation with ROC -- Comparison of Results with Other Studies on Colonography -- Computational Speed and Scalability Evaluation of GKFA -- Conclusions -- References -- Online Kernel Analysis -- Introduction -- Kernel Basics: A Brief Review -- Kernel Principal Component Analysis -- Kernel Selection -- Kernel Adaptation Analysis of PC-KFA -- Heterogeneous vs. Homogeneous Data for Online PC-KFA -- Updating the Gram Matrix of the Online Data -- Composite Kernel for Online Data -- Long-Term Sequential Trajectories with Self-Monitoring -- Reevaluation of Large Online Data -- Validation of Decomposing Online Data into Small Chunks -- Experimental Results -- Cancer Datasets -- Selection of Optimum Kernel and Composite Kernel for Offline Data I -- Selection of Optimum Kernel and Composite Kernel for the New Online Sequences -- Classification of Heterogeneous Versus Homogeneous Data -- Online Learning Evaluation of Long-term Sequence -- Evaluation of Computational Time -- Conclusions -- References -- Cloud Kernel Analysis -- Introduction -- Cloud Environments -- Server Specifications of Cloud Platforms -- Cloud Framework of KPCA for AMD -- AMD for Cloud Colonography -- AMD Concept -- Data Configuration of AMD -- Implementation of AMD for Two Cloud Cases -- Parallelization of AMD -- Classification Evaluation of Cloud Colonography -- Databases with Classification Criteria -- Classification Results -- Cloud Computing Performance -- Cloud Computing Setting with Cancer Databases -- Computation Time -- Memory Usage -- Running Cost -- Parallelization -- Conclusions -- References -- Predictive Kernel Analysis -- Introduction -- Kernel Basics -- KPCA and AKFA -- Stationary Data Training -- Kernel Selection -- Composite Kernel: Kernel Combinatory Optimization -- Longitudinal Nonstationary Data with Anomaly/Normal Detection -- Updating the Gram Matrix Based on Nonstationary Longitudinal Data -- Composite Kernel for Nonstationary Data -- Longitudinal Sequential Trajectories for Anomaly Detection and Prediction -- Anomaly Detection of Nonstationary Small Chunks Datasets -- Anomaly Prediction of Long-Time Sequential Trajectories -- Classification Results -- Cancer Datasets -- Selection of Optimum Kernel and Composite Kernel for Stationary Data -- Comparisons with Other Kernel Learning Methods -- Anomaly Detection for the Nonstationary Data -- Longitudinal Prediction Results -- Large Nonstationary Sequential dataset for Anomaly Detection -- Time Horizontal Prediction for Risk Factor Analysis of Anomaly Long-Time Sequential Trajectories -- Computational Time for Complexity Evaluation -- Conclusions -- References -- Conclusion
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| Abstract
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"This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications"--
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| Subject
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Kernel functions.
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| Subject
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Big data-- Mathematics.
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| Dewey Classification
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515/.9
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| LC Classification
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QA353.K47
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| Added Entry
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Ohio Library and Information Network.
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