Reinforcement and Systemic Machine Learning for Decision Making

 
9788126556250: Reinforcement and Systemic Machine Learning for Decision Making

Reinforcement and Systemic Machine Learning for Decision Making- Wiley India-Parag Kulkarni-2015-EDN-1

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Parag Kulkarni
ISBN 10: 8126556250 ISBN 13: 9788126556250
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Book Description 2015. Soft cover. Book Condition: New. International Edition. Book Condition: BRAND NEW. International/Eastern Economy Edition, Paperback/Softcover with SAME TITLE, AUTHOR AND EDITION as listed. ISBN and Cover design differs. **100% IDENTICAL CONTENTS as U.S Edition**. Standard Delivery within 7-14 business days ACROSS THE GLOBE. We can ship to PO Box, APO address in US. International Edition Textbooks may bear a label (Not for sale in the U.S. or Canada) or (For sale in Asia only) or similar restrictions- printed only to discourage students from obtaining an affordable copy. US Court has asserted your right to buy and use International edition. Access code/CD may not provided with these editions. We may ship the books from MULTIPLE WAREHOUSES ACROSS THE GLOBE including Asia depending upon the availability of inventory. Printed in English. Customer satisfaction guaranteed. Bookseller Inventory # ABECA8030

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Parag Kulkarni
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Book Description Wiley. Paperback. Book Condition: New. The book focuses on machine learning and systemic machine learning -- a specialized research area in the field of machine learning. It also covers reinforcement learning and advanced developments in this field. There are always difficulties in making machines to learn from experiences. Further always while learning the complete information is not available and it becomes available in bits and pieces over the period of time. The information is even collected and processed from a particular perspective. As per systemic learning there is need to understand impact of decisions and actions on system and that to over the period of time. Bookseller Inventory # WILEY-9788126556250

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Parag Kulkarni
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ISBN 10: 8126556250 ISBN 13: 9788126556250
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Book Description Wiley. Paperback. Book Condition: New. The book focuses on machine learning and systemic machine learning -- a specialized research area in the field of machine learning. It also covers reinforcement learning and advanced developments in this field. There are always difficulties in making machines to learn from experiences. Further always while learning the complete information is not available and it becomes available in bits and pieces over the period of time. The information is even collected and processed from a particular perspective. As per systemic learning there is need to understand impact of decisions and actions on system and that to over the period of time. Bookseller Inventory # WILEY-9788126556250

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Parag Kulkarni
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Book Description Softcover. Book Condition: New. 1st edition. Brand NEW, Paperback International Edition. Black & White or color, Cover and ISBN may be different but similar contents as US editions. Standard delivery takes 5-9 business days by USPS with tracking number. Choose expedited shipping for superfast delivery 2-4 business days by DHL/FEDEX. We also ship to PO Box addresses but by Standard delivery. International Edition Textbooks may bear a label -Not for sale in the U.S. or Canada- etc. printed only to discourage U.S. students from obtaining an affordable copy. Legal to use despite any disclaimer on cover as per US court. No access code or CD included unless specified. In some instances, the international textbooks may have different exercises at the end of the chapters. Printed in English. We may ship the books from multiple warehouses across the globe, including India depending upon the availability of inventory storage. 100% Customer satisfaction guaranteed! Please feel free to contact us for any queries. Bookseller Inventory # LPBDIN41560893

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Parag Kulkarni
Published by Wiley India Pvt. Ltd (2015)
ISBN 10: 8126556250 ISBN 13: 9788126556250
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Book Description Wiley India Pvt. Ltd, 2015. Softcover. Book Condition: New. First edition. The book focuses on machine learning and systemic machine learning -- a specialized research area in the field of machine learning. It also covers reinforcement learning and advanced developments in this field. There are always difficulties in making machines to learn from experiences. Further always while learning the complete information is not available and it becomes available in bits and pieces over the period of time. The information is even collected and processed from a particular perspective. As per systemic learning there is need to understand impact of decisions and actions on system and that to over the period of time. Preface Acknowledgments About the Author 1 Introduction to Reinforcement and Systemic Machine Learning 1.1. Introduction 1.2. Supervised, Unsupervised, and Semi supervised Machine Learning 1.3. Traditional Learning Methods and History of Machine Learning 1.4. What Is Machine Learning? 1.5. Machine-Learning Problem 1.6. Learning Paradigms 1.7. Machine-Learning Techniques and Paradigms 1.8. What Is Reinforcement Learning? 1.9. Reinforcement Function and Environment Function 1.10. Need of Reinforcement Learning 1.11. Reinforcement Learning and Machine Intelligence 1.12. What is Systemic Learning? 1.13. What is Systemic Machine Learning? 1.14. Challenges in Systemic Machine Learning 1.15. Reinforcement Machine Learning and Systemic Machine Learning 1.16. Case Study Problem Detection in a Vehicle 1.17. Summary 2 Fundamentals of Whole-System, Systemic and Multiperspective Machine Learning 2.1. Introduction 2.2. What Is Systemic Machine Learning? 2.3. Generalized Systemic Machine-Learning Framework 2.4. Multiperspective Decision Making and Multiperspective Learning 2.5. Dynamic and Interactive Decision Making 2.6. The Systemic Learning Framework 2.7. System Analysis 2.8. Case Study: Need of Systemic Learning in the Hospitality Industry 2.9. Summary 3 Reinforcement Learning 3.1. Introduction 3.2. Learning Agents 3.3. Returns and Reward Calculations 3.4. Reinforcement Learning and Adaptive Control 3.5. Dynamic Systems 3.6. Reinforcement Learning and Control 3.7. Markov Property and Markov Decision Process 3.8. Value Functions 3.9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 3.10. Dynamic Programming 3.11. Adaptive Dynamic Programming 3.12. Example: Reinforcement Learning for Boxing Trainer 3.13. Summary 4 Systemic Machine Learning and Model 4.1. Introduction 4.2. A Framework for Systemic Learning 4.3. Capturing the Systemic View 4.4. Mathematical Representation of System Interactions 4.5. Impact Function 4.6. Decision-Impact Analysis 4.7. Summary 5 Inference and Information Integration 5.1. Introduction 5.2. Inference Mechanisms and Need 5.3. Integration of Context and Inference 5.4. Statistical Inference and Induction 5.5. Pure Likelihood Approach 5.6. Bayesian Paradigm and Inference 5.7. Time-Based Inference 5.8. Inference to Build a System View 5.9. Summary 6 Adaptive Learning 6.1. Introduction 6.2. Adaptive Learning and Adaptive Systems 6.3. What Is Adaptive Machine Learning? 6.4. Adaptation and Learning Method Selection Based on Scenario 6.5. Systemic Learning and Adaptive Learning 6.6. Competitive Learning and Adaptive Learning 6.7. Examples 6.8. Summary 7 Multiperspective and Whole-System Learning 7.1. Introduction 7.2. Multiperspective Context Building 7.3. Multiperspective Decision Making and Multiperspective Learning 7.4. Whole-System Learning and Multiperspective Approaches 7.5. Case Study Based on Multiperspective Approach 7.6. Limitations to a Multiperspective Approach 7.7. Summary 8 Incremental Learning and Knowledge Representation 8.1. Introduction 8.2. Why Incremental Learning? 8.3. Learning from What Is Already Learned. . . 8.4. Supervised Incremental Learning 8.5. Incremental Unsupervised Learning and Incremental Clustering 8.6 Printed Pages: 312. Bookseller Inventory # 98353

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Parag Kulkarni
Published by Wiley India Pvt. Ltd (2015)
ISBN 10: 8126556250 ISBN 13: 9788126556250
New Softcover First Edition Quantity Available: > 20
Seller:
A - Z Books
(New Delhi, DELHI, India)
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Book Description Wiley India Pvt. Ltd, 2015. Softcover. Book Condition: New. First edition. The book focuses on machine learning and systemic machine learning -- a specialized research area in the field of machine learning. It also covers reinforcement learning and advanced developments in this field. There are always difficulties in making machines to learn from experiences. Further always while learning the complete information is not available and it becomes available in bits and pieces over the period of time. The information is even collected and processed from a particular perspective. As per systemic learning there is need to understand impact of decisions and actions on system and that to over the period of time. Preface Acknowledgments About the Author 1 Introduction to Reinforcement and Systemic Machine Learning 1.1. Introduction 1.2. Supervised, Unsupervised, and Semi supervised Machine Learning 1.3. Traditional Learning Methods and History of Machine Learning 1.4. What Is Machine Learning? 1.5. Machine-Learning Problem 1.6. Learning Paradigms 1.7. Machine-Learning Techniques and Paradigms 1.8. What Is Reinforcement Learning? 1.9. Reinforcement Function and Environment Function 1.10. Need of Reinforcement Learning 1.11. Reinforcement Learning and Machine Intelligence 1.12. What is Systemic Learning? 1.13. What is Systemic Machine Learning? 1.14. Challenges in Systemic Machine Learning 1.15. Reinforcement Machine Learning and Systemic Machine Learning 1.16. Case Study Problem Detection in a Vehicle 1.17. Summary 2 Fundamentals of Whole-System, Systemic and Multiperspective Machine Learning 2.1. Introduction 2.2. What Is Systemic Machine Learning? 2.3. Generalized Systemic Machine-Learning Framework 2.4. Multiperspective Decision Making and Multiperspective Learning 2.5. Dynamic and Interactive Decision Making 2.6. The Systemic Learning Framework 2.7. System Analysis 2.8. Case Study: Need of Systemic Learning in the Hospitality Industry 2.9. Summary 3 Reinforcement Learning 3.1. Introduction 3.2. Learning Agents 3.3. Returns and Reward Calculations 3.4. Reinforcement Learning and Adaptive Control 3.5. Dynamic Systems 3.6. Reinforcement Learning and Control 3.7. Markov Property and Markov Decision Process 3.8. Value Functions 3.9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 3.10. Dynamic Programming 3.11. Adaptive Dynamic Programming 3.12. Example: Reinforcement Learning for Boxing Trainer 3.13. Summary 4 Systemic Machine Learning and Model 4.1. Introduction 4.2. A Framework for Systemic Learning 4.3. Capturing the Systemic View 4.4. Mathematical Representation of System Interactions 4.5. Impact Function 4.6. Decision-Impact Analysis 4.7. Summary 5 Inference and Information Integration 5.1. Introduction 5.2. Inference Mechanisms and Need 5.3. Integration of Context and Inference 5.4. Statistical Inference and Induction 5.5. Pure Likelihood Approach 5.6. Bayesian Paradigm and Inference 5.7. Time-Based Inference 5.8. Inference to Build a System View 5.9. Summary 6 Adaptive Learning 6.1. Introduction 6.2. Adaptive Learning and Adaptive Systems 6.3. What Is Adaptive Machine Learning? 6.4. Adaptation and Learning Method Selection Based on Scenario 6.5. Systemic Learning and Adaptive Learning 6.6. Competitive Learning and Adaptive Learning 6.7. Examples 6.8. Summary 7 Multiperspective and Whole-System Learning 7.1. Introduction 7.2. Multiperspective Context Building 7.3. Multiperspective Decision Making and Multiperspective Learning 7.4. Whole-System Learning and Multiperspective Approaches 7.5. Case Study Based on Multiperspective Approach 7.6. Limitations to a Multiperspective Approach 7.7. Summary 8 Incremental Learning and Knowledge Representation 8.1. Introduction 8.2. Why Incremental Learning? 8.3. Learning from What Is Already Learned. . . 8.4. Supervised Incremental Learning 8.5. Incremental Unsupervised Learning and Incremental Clustering 8.6 Printed Pages: 312. Bookseller Inventory # 98353

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Parag Kulkarni
Published by Wiley India
ISBN 10: 8126556250 ISBN 13: 9788126556250
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Book Description Wiley India. softcover. Book Condition: Brand New. International Edition. International Edition, Cover & ISBN may be different from US edition, PAPERBACK. Book Condition: New. Brand New,Softcover,No Access code & No context dvd, But Contents are same as US Edition. Printed in English Language, Prompt shipping by USPS/,UPS/,DHL/,FedEx. Exceptional customer Service, Satisfaction Guaranteed. We may ship the books from Asian regions for inventory purpose. "Special Note" We do provide service on APO BOX & PO BOX addresses only in usa. Bookseller Inventory # adhiya11822

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