Human Behavior Learning and Transfer Yangsheng Xu
Bridging the gap between human-computer engineering and control engineering, Human Behavior Learning and Transfer delineates how to abstract human action and reaction skills into computational models. The authors include methods for modeling a variety of human action and reaction behaviors and explo...
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| Ngôn ngữ: | English |
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CRC Press
2009
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| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1479 |
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| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
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oai:scholar.dlu.edu.vn:DLU123456789-1479 |
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Thư viện Trường Đại học Đà Lạt |
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English |
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Bridging the gap between human-computer engineering and control engineering, Human Behavior Learning and Transfer delineates how to abstract human action and reaction skills into computational models. The authors include methods for modeling a variety of human action and reaction behaviors and explore processes for evaluating, optimizing, and transferring human skills. They also cover modeling continuous and discontinuous human control strategy and discuss simulation studies and practical real-life situations.
The book examines how to model two main aspects of human behavior: reaction skills and action skills. It begins with a discussion of the various topics involved in human reaction skills modeling. The authors apply machine learning techniques and statistical analysis to abstracting models of human reaction control strategy. They contend that such models can be learned sufficiently to emulate complex human control behaviors in the feedback loop.
The second half of the book explores issues related to human action skills modeling. The methods presented are based on techniques for reducing the dimensionality of data sets, while preserving as much useful information as possible. The modeling approaches developed are applied in real-life applications including navigation of smart wheel chairs and intelligent surveillance.
Written in a consistent, easily approachable style, the book includes in-depth discussions of a broad range of topics. It provides the tools required to formalize human behaviors into algorithmic, machine-coded strategies. |
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Book |
| author |
Xu, Yangsheng C. Lee, Ka Keung |
| spellingShingle |
Xu, Yangsheng C. Lee, Ka Keung Human Behavior Learning and Transfer Yangsheng Xu |
| author_facet |
Xu, Yangsheng C. Lee, Ka Keung |
| author_sort |
Xu, Yangsheng |
| title |
Human Behavior Learning and Transfer
Yangsheng Xu |
| title_short |
Human Behavior Learning and Transfer
Yangsheng Xu |
| title_full |
Human Behavior Learning and Transfer
Yangsheng Xu |
| title_fullStr |
Human Behavior Learning and Transfer
Yangsheng Xu |
| title_full_unstemmed |
Human Behavior Learning and Transfer
Yangsheng Xu |
| title_sort |
human behavior learning and transfer
yangsheng xu |
| publisher |
CRC Press |
| publishDate |
2009 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1479 |
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1819790811871576064 |
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oai:scholar.dlu.edu.vn:DLU123456789-14792009-12-02T09:31:02Z Human Behavior Learning and Transfer Yangsheng Xu Xu, Yangsheng C. Lee, Ka Keung Bridging the gap between human-computer engineering and control engineering, Human Behavior Learning and Transfer delineates how to abstract human action and reaction skills into computational models. The authors include methods for modeling a variety of human action and reaction behaviors and explore processes for evaluating, optimizing, and transferring human skills. They also cover modeling continuous and discontinuous human control strategy and discuss simulation studies and practical real-life situations. The book examines how to model two main aspects of human behavior: reaction skills and action skills. It begins with a discussion of the various topics involved in human reaction skills modeling. The authors apply machine learning techniques and statistical analysis to abstracting models of human reaction control strategy. They contend that such models can be learned sufficiently to emulate complex human control behaviors in the feedback loop. The second half of the book explores issues related to human action skills modeling. The methods presented are based on techniques for reducing the dimensionality of data sets, while preserving as much useful information as possible. The modeling approaches developed are applied in real-life applications including navigation of smart wheel chairs and intelligent surveillance. Written in a consistent, easily approachable style, the book includes in-depth discussions of a broad range of topics. It provides the tools required to formalize human behaviors into algorithmic, machine-coded strategies. INTRODUCTION Motivation Overview INTRODUCTION TO HUMAN REACTION SKILL MODELING Motivation Related Work LEANING OF HUMAN CONTROL STRATEGY: CONTINUOUS AND DISCONTINUOUS Experimental Design Cascade Neural Networks with Kalman Filtering HCS Models: Continuous Control HCS Models: Discontinuous Control VALIDATION OF HUMAN CONTROL STRATEGY MODELS Need for Model Validation Stochastic Similarity Measure Human-to-Model Comparisons EVALUATION OF HUMAN CONTROL STRATEGY Introduction Obstacle Avoidance Tight Turning Transient Response Time Delay Passenger Comfort Driving Smoothness Summary PERFORMANCE OPTIMIZATION OF HUMAN CONTROL STRATEGY Introduction Simultaneously Perturbed Stochastic Approximation Iterative Optimization Algorithm Model Optimization and Performance Analysis Summary TRANSFER OF HUMAN CONTROL STRATEGY Introduction Model Transfer Based on Similarity Measure Model Compensation Summary TRANSFERRING HUMAN NAVIGATIONAL SKILLS TO SMART WHEELCHAIR Introduction Methodology Experimental Study Analysis Conclusion INTRODUCTION TO HUMAN ACTION SKILL MODELING Learning Action Models Dimension Reduction Formulation Related Research GLOBAL PARAMETRIC METHODS FOR DIMENSION REDUCTION Introduction Parametric Methods for Global Modeling An Experimental Data Set PCA for Modeling Performance Data NLPCA SNLPCA Comparison Characterizing NLPCA Mappings LOCAL METHODS FOR DIMENSION REDUCTION Introduction Non-parametric Methods for Trajectory Fitting Scatter Plot Smoothing Action Recognition Using Smoothing Splines An Experiment Using Spline Smoothing Principal Curves Expanding the One-Dimensional Representation Branching Over-Fitting A SPLINE SMOOTHER IN PHASE SPACE FOR TRAJECTORY FITTING Smoothing with Velocity Information Problem Formulation Solution Notes on Computation and Complexity Similar Parameterizations Multi-Dimensional Smoothing Estimation of Variances Windowing Variance Estimates The Effect of Velocity Information Cross-Validation ANALYSIS OF HUMAN WALKING TRAJECTORIES FOR SURVEILLANCE Introduction System Overview Background Subtraction Global Trajectory Similarity Estimation Trajectory Normality Classifier Experiment 1: Trajectory Normality Classifier Further Analysis on Global Trajectory Similarity Based on LCSS Methodology Used in Boundary Modeling LCSS Boundary Limit Establishment Experiment 2: Boundary Modeling Discussion Conclusions MODELING OF FACIAL AND FULL-BODY ACTIONS Facial Expression Intensity Modeling Full-Body Action Modeling CONCLUSIONS 2009-12-02T09:31:02Z 2009-12-02T09:31:02Z 2005 Book https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/1479 en application/rar CRC Press |