Reinforcement and systemic machine learning for decision making /

Reinforcement and systemic machine learning for decision making /

Reinforcement and Systemic Machine Learning for Decision MakingThere are always difficulties in making machines that learn from experience. Complete information is not always available--or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a ne...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Kulkarni, Parag
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Hoboken : John Wiley & Sons, [2012]
Σειρά:IEEE Press series on systems science and engineering.
Θέματα:
Reinforcement learning.
Machine learning.
Decision making.
Decision Making.
TECHNOLOGY & ENGINEERING > Electronics > General.
Science.
Computer science.
COMPUTERS > Enterprise Applications > Business Intelligence Tools.
COMPUTERS > Intelligence (AI) & Semantics.
Electronic books.
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Kulkarni, Parag. 
245 1 0 |a Reinforcement and systemic machine learning for decision making /  |c Parag Kulkarni. 
264 1 |a Hoboken :  |b John Wiley & Sons,  |c [2012] 
264 4 |c ©2012 
300 |a 1 online resource (422 pages). 
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 IEEE Press Series on Systems Science and Engineering ;  |v v. 1 
505 0 0 |g ch. 1:  |t Introduction to Reinforcement and Systemic Machine Learning --  |g 1.1.  |t Introduction --  |g 1.2.  |t Supervised, Unsupervised, and Semisupervised Machine Learning --  |g 1.3.  |t Traditional Learning Methods and History of Machine Learning --  |g 1.4.  |t What is Machine Learning? --  |g 1.5.  |t Machine-Learning Problem --  |g 1.6.  |t Learning Paradigms --  |g 1.7.  |t Machine-Learning Techniques and Paradigms --  |g 1.8.  |t What is Reinforcement Learning? --  |g 1.9.  |t Reinforcement Function and Environment Function --  |g 1.10.  |t Need of Reinforcement Learning --  |g 1.11.  |t Reinforcement Learning and Machine Intelligence --  |g 1.12.  |t What is Systemic Learning? --  |g 1.13.  |t What Is Systemic Machine Learning? --  |g 1.14.  |t Challenges in Systemic Machine Learning --  |g 1.15.  |t Reinforcement Machine Learning and Systemic Machine Learning --  |g 1.16.  |t Case Study Problem Detection in a Vehicle --  |g 1.17.  |t Summary --  |g Reference. 
505 8 0 |g ch. 2:  |t Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning --  |g 2.1.  |t Introduction --  |g 2.2.  |t What is Systemic Machine Learning? --  |g 2.3.  |t Generalized Systemic Machine-Learning Framework --  |g 2.4.  |t Multiperspective Decision Making and Multiperspective Learning --  |g 2.5.  |t Dynamic and Interactive Decision Making --  |g 2.6.  |t The Systemic Learning Framework --  |g 2.7.  |t System Analysis --  |g 2.8.  |t Case Study:  |t Need of Systemic Learning in the Hospitality Industry --  |g 2.9.  |t Summary. 
505 8 0 |g ch. 3.  |t :  |t Reinforcement Learning --  |g 3.1.  |t Introduction --  |g 3.2.  |t Learning Agents --  |g 3.3.  |t Returns and Reward Calculations --  |g 3.4.  |t Reinforcement Learning and Adaptive Control --  |g 3.5.  |t Dynamic Systems --  |g 3.6.  |t Reinforcement Learning and Control --  |g 3.7.  |t Markov Property and Markov Decision Process --  |g 3.8.  |t Value Functions --  |g 3.9.  |t Learning An Optimal Policy (Model-Based and Model-Free Methods) --  |g 3.10.  |t Dynamic Programming --  |g 3.11.  |t Adaptive Dynamic Programming --  |g 3.12.  |t Example:  |t Reinforcement Learning for Boxing Trainer --  |g 3.13.  |t Summary --  |g Reference. 
505 8 0 |g ch. 4:  |t Systemic Machine Learning and Model --  |g 4.1.  |t Introduction --  |g 4.2.  |t A Framework for Systemic Learning --  |g 4.3.  |t Capturing THE Systemic View --  |g 4.4.  |t Mathematical Representation of System Interactions --  |g 4.5.  |t Impact Function --  |g 4.6.  |t Decision-Impact Analysis --  |g 4.7.  |t Summary. 
505 8 0 |g ch. 5:  |t Inference and Information Integration --  |g 5.1.  |t Introduction --  |g 5.2.  |t Inference Mechanisms and Need --  |g 5.3.  |t Integration of Context and Inference --  |g 5.4.  |t Statistical Inference and Induction --  |g 5.5.  |t Pure Likelihood Approach --  |g 5.6.  |t Bayesian Paradigm and Inference --  |g 5.7.  |t Time-Based Inference --  |g 5.8.  |t Inference to Build a System View --  |g 5.9.  |t Summary. 
505 8 0 |g ch. 6:  |t Adaptive Learning --  |g 6.1.  |t Introduction --  |g 6.2.  |t Adaptive Learning and Adaptive Systems --  |g 6.3.  |t What is Adaptive Machine Learning? --  |g 6.4.  |t Adaptation and Learning Method Selection Based on Scenario --  |g 6.5.  |t Systemic Learning and Adaptive Learning --  |g 6.6.  |t Competitive Learning and Adaptive Learning --  |g 6.7.  |t Examples --  |g 6.8.  |t Summary. 
505 8 0 |g ch. 7:  |t Multiperspective and Whole-System Learning --  |g 7.1.  |t Introduction --  |g 7.2.  |t Multiperspective Context Building --  |g 7.3.  |t Multiperspective Decision Making and Multiperspective Learning --  |g 7.4.  |t Whole-System Learning and Multiperspective Approaches --  |g 7.5.  |t Case Study Based on Multiperspective Approach --  |g 7.6.  |t Limitations to a Multiperspective Approach --  |g 7.7.  |t Summary. 
505 8 0 |g ch. 8:  |t Incremental Learning and Knowledge Representation --  |g 8.1.  |t Introduction --  |g 8.2.  |t Why Incremental Learning? --  |g 8.3.  |t Learning from What Is Already Learned --  |g 8.4.  |t Supervised Incremental Learning --  |g 8.5.  |t Incremental Unsupervised Learning and Incremental Clustering --  |g 8.6.  |t Semisupervised Incremental Learning --  |g 8.7.  |t Incremental and Systemic Learning --  |g 8.8.  |t Incremental Closeness Value and Learning Method --  |g 8.9.  |t Learning and Decision-Making Model --  |g 8.10.  |t Incremental Classification Techniques --  |g 8.11.  |t Case Study: Incremental Document Classification --  |g 8.12.  |t Summary. 
505 8 0 |g ch. 9 Knowledge Augmentation: A Machine Learning Perspective --  |g 9.1.  |t Introduction --  |g 9.2.  |t Brief History and Related Work --  |g 9.3.  |t Knowledge Augmentation and Knowledge Elicitation --  |g 9.4.  |t Life Cycle of Knowledge --  |g 9.5.  |t Incremental Knowledge Representation --  |g 9.6.  |t Case-Based Learning and Learning with Reference Knowledge Loss --  |g 9.7.  |t Knowledge Augmentation: Techniques and Methods --  |g 9.8.  |t Heuristic Learning --  |g 9.9.  |t Systemic Machine Learning and Knowledge Augmentation --  |g 9.10.  |t Knowledge Augmentation in Complex Learning Scenarios --  |g 9.11.  |t Case Studies --  |g 9.12.  |t Summary. 
505 8 0 |g ch. 10:  |t Building a Learning System --  |g 10.1.  |t Introduction --  |g 10.2.  |t Systemic Learning System --  |g 10.3.  |t Algorithm Selection --  |g 10.4.  |t Knowledge Representation --  |g 10.4.1.  |t Practical Scenarios and Case Study --  |g 10.5.  |t Designing a Learning System --  |g 10.6.  |t Making System to Behave Intelligently --  |g 10.7.  |t Example-Based Learning --  |g 10.8.  |t Holistic Knowledge Framework and Use of Reinforcement Learning --  |g 10.9.  |t Intelligent Agents Deployment and Knowledge Acquisition and Reuse --  |g 10.10.  |t Case-Based Learning: Human Emotion-Detection System --  |g 10.11.  |t Holistic View in Complex Decision Problem --  |g 10.12.  |t Knowledge Representation and Data Discovery --  |g 10.13.  |t Components --  |g 10.14.  |t Future of Learning Systems and Intelligent Systems --  |g 10.15.  |t Summary --  |g Appendix A:  |t Statistical Learning Methods --  |g Appendix B:  |t Markov Processes. 
520 |a Reinforcement and Systemic Machine Learning for Decision MakingThere are always difficulties in making machines that learn from experience. Complete information is not always available--or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm--creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and g. 
588 0 |a Print version record. 
650 0 |a Reinforcement learning. 
650 0 |a Machine learning. 
650 0 |a Decision making. 
650 4 |a Decision Making. 
650 4 |a TECHNOLOGY & ENGINEERING  |x Electronics  |x General. 
650 4 |a Science. 
650 4 |a Computer science. 
650 7 |a COMPUTERS  |x Enterprise Applications  |x Business Intelligence Tools.  |2 bisacsh 
650 7 |a COMPUTERS  |x Intelligence (AI) & Semantics.  |2 bisacsh 
650 7 |a Decision making.  |2 fast  |0 (OCoLC)fst00889035 
650 7 |a Machine learning.  |2 fast  |0 (OCoLC)fst01004795 
650 7 |a Reinforcement learning.  |2 fast  |0 (OCoLC)fst01732553 
655 4 |a Electronic books. 
655 7 |a Electronic books.  |2 local 
776 0 8 |i Print version:  |a Kulkarni, Parag.  |t Reinforcement and Systemic Machine Learning for Decision Making.  |d Hoboken : John Wiley & Sons, ©2012  |z 9780470919996 
830 0 |a IEEE Press series on systems science and engineering. 
856 4 0 |u https://doi.org/10.1002/9781118266502  |z Full Text via HEAL-Link 
994 |a 92  |b DG1 

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