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Choice Computing
| Choice Computing |
| Autore | Kulkarni Parag |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2022 |
| Descrizione fisica | 1 online resource (254 pages) |
| Disciplina | 658.83420285631 |
| Collana | Intelligent Systems Reference Library |
| ISBN |
9789811940590
9789811940583 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Foreword -- Preface: Embarking on the Journey of Choice… -- In Gratitude -- Praise for Choice Computing: Machine Learning and Systemic Economics for Choosing -- Contents -- About the Author -- 1 Introduction: Choosing-What is a Great Deal? -- 1.1 Choosing -- 1.2 Choosing and Learning -- 1.3 Organization of Book -- 1.4 Before Moving Ahead -- 1.5 Knowing What is It About -- References -- 2 Choice Modelling: Where Choosing Meets Computing -- 2.1 Introduction-Unfolding Choice Economics and Choice Computing -- 2.2 Mathematics of Choosing -- 2.3 Economic Impact of Choosing -- 2.4 Choice Paths -- 2.4.1 Three Pillars of Choosing -- 2.5 Rational Choices -- 2.6 Basics of Artificial Intelligence (AI) and Machine Learning (ML) -- 2.6.1 Traditional Algorithms to Reinforcement Learning -- 2.7 Bio-inspired Machine Learning -- 2.8 Choosing Inspired Machine Learning -- 2.9 Philosophy of Choosing -- 2.10 Context-Based ML -- 2.11 Choosing: Manifestation of Freedom, Youthfulness and Intelligence -- 2.11.1 When We Choose Versus When We Select -- 2.11.2 Voluntary Activities -- 2.11.3 Sapiens' Choice Making Resulting in Survival and Supremacy -- 2.11.4 Choosy Innovators -- 2.11.5 Choosing to Become Successful (Goal-Driven Systems) -- 2.12 Empowering Others to Choose -- 2.13 Cost Associated with Choosing -- 2.14 Choose, Let Others Choose and Empower Them to Choose -- 2.15 Choice Architects -- 2.16 Choice Models-Looking at Choosing as a Constraint Satisfaction Problem -- 2.17 Dynamic and Static Choice Models -- 2.18 Uncertainty and Choosing -- 2.18.1 Choice Experiments -- 2.18.2 Top of Mountain and Hazy Glass Theory -- 2.18.3 Seed-Based Exploration -- 2.19 Summary -- References -- 3 ML of Choosing: Architecting Intelligent Choice Framework -- 3.1 Who is a Choice Architect? -- 3.2 Stories Choice Architects -- 3.3 Those Who Help You to Choose.
3.4 Architecting Choice Routes -- 3.5 Choice Flow -- 3.6 Choice Architecture to Revolutionize Thinking -- 3.7 Mastering Choice Architecting: Associating Algorithms -- 3.8 Creating Logical Choices for Customers: -- 3.9 Making Customers to Choose What You Would Love to Choose Them -- 3.10 Context-Based Choice Making and Scenario Analysis -- 3.11 Systemic Choice Architect -- 3.12 Summary -- References -- 4 Machine Learning of Choice Economics -- 4.1 ML of Choice Economics -- 4.2 Learning Based on the Impact of Choosing -- 4.3 Creating Experiential Bias or Availability Bias for Learning -- 4.4 Event Anchoring-Based Learning -- 4.4.1 Event Sequencing -- 4.5 First Movers … -- 4.6 Creation of Legal Choices and Learning by Choice Elimination -- 4.7 Choice Impact-Based Learning -- 4.8 Learning to Set Target for Choosing -- 4.8.1 Leverage Point-Based Learning -- 4.9 Learning Based on Impact of Choosing -- 4.9.1 Rules for Choosing-Based ML -- 4.10 Choice Evolution -- 4.10.1 Evolutionary Choice Systems -- 4.10.2 Choice-Driven Crossover -- 4.10.3 Choice Association -- 4.10.4 Competitive Greedy Choosing -- 4.11 Choice Making in Uncertain Scenario -- 4.12 Core Choices and Supporting Choices (Decision About Learning Points) -- 4.13 Choice Projections-Connecting Peaks of Mountains (Multi-goal Architecture) -- 4.13.1 Choice Intelligence and Choice Processing -- 4.13.2 Societal Choice Computing -- 4.14 Conformation Choice Computing -- 4.14.1 Machine Learning Models -- 4.15 Summary -- References -- 5 Co-operative Choosing: Machines and Humans Thinking Together to Choose the Right Way -- 5.1 Introduction -- 5.2 Co-operative Choosing (Choosing Together) -- 5.3 Choice Co-operation -- 5.3.1 Learning to Choose -- 5.4 Cognitive Choice Models -- 5.5 Competitive, Ranking and Hybrid Models in Co-operative Choosing -- 5.6 Utility Theory for Choosing. 5.7 Co-operative Greedy Choice Traversal -- 5.8 Choice Models -- 5.8.1 Causal Cognition -- 5.8.2 Unifying Cognition -- 5.8.3 Binary Choice Instinct -- 5.8.4 Data, Average, Spread and Instinct: Decoding Mean, Median and Distribution of Choosing -- 5.8.5 Associative Choice Models -- 5.8.6 Entropy-Based Choice Models -- 5.8.7 Discrete Choice Models -- 5.8.8 Weighted Additive Choice Models -- 5.8.9 Inter Temporal Choice Models -- 5.8.10 Random Utility Choice Models -- 5.8.11 Hierarchical Choice Models -- 5.8.12 Co-operative Choice Models -- 5.8.13 Randomness in Choosing -- 5.9 Co-operative Choosing to Escape from Noise and Still Preserving Diversity -- 5.10 Summary -- References -- 6 Choice Architecture-Machine Learning Framework -- 6.1 Choice Architecture -- 6.2 Choice Catalyst Algorithm -- 6.3 Choice Architecture and Machine Learning -- 6.4 Identifying Chance Maximization Point -- 6.5 Option Eliminator-Learning to Eliminate -- 6.6 Embedding Emotions, Kansei Engineering (Emotional Computing for Choosing) -- 6.7 State Transitions to 'Choice-State' -- 6.8 Choice Learning Models -- 6.9 Behavioural System and Choosing -- 6.10 Choice Learning-Based Recommender System -- 6.11 Multi Choice Scenarios -- 6.12 Summary -- References -- 7 Artificial Consciousness and Choosing (Towards Conscious Choice Machines) -- 7.1 Introduction -- 7.2 Decoding Consciousness of Choosing -- 7.3 Artificial Conscious Choice Agent -- 7.4 Designing a Conscious Choice Agent (CCA) -- 7.5 Heuristic Choice Strategies -- 7.6 Exploratory Consciousness -- 7.7 Choice Architecting and Recommending Products or Services -- 7.8 Conscious Choice Architecting -- 7.9 Conscious Choice and Evolutionary Learning -- 7.10 Reinforcement and Deep Reinforcement Learning -- 7.11 Dealing with Local Maxima and Minima -- 7.12 Summary -- References -- 8 Choice Computing and Creativity. 8.1 Introduction-Creative Contributions: Human Choosing and Machine Choosing -- 8.2 Human Creative Choosing Process -- 8.3 Concept Learning and Verbal Learning for Choosing -- 8.4 What is the Difference Between Choice and a Creative Choice? -- 8.5 Creative Choosing Machines -- 8.6 Creative Choice Agents -- 8.7 Discrimination Learning and Creative Choosing -- 8.8 Unconscious Blind Choosing to Unconscious Effective Choosing -- 8.9 Creative Choosing Models -- 8.10 Concept Maps for Choosing -- 8.11 Human Learning Inspired Creative Machine-Choosing Models -- 8.11.1 Reinforcement Choice Models -- 8.12 Creative Choice Learning Models -- 8.13 Creativity Moments and Creativity Points -- 8.14 Creative Agents and Creative Collaborative Intelligence -- 8.15 Summary -- References -- 9 Experimental Choice Computing and Choice Learning Through Real-Life Stories -- 9.1 Summary -- 9.2 In Education -- 9.3 Health Care -- 9.4 Social Good -- 9.5 Finance -- 9.6 Miscellaneous -- 9.7 Other Applications Can Be Thought of -- 9.8 Summary -- References -- 10 Choice Computing and Beyond -- Index. |
| Record Nr. | UNINA-9910590053703321 |
Kulkarni Parag
|
||
| Singapore : , : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reinforcement and systemic machine learning for decision making / / Parag Kulkarni
| Reinforcement and systemic machine learning for decision making / / Parag Kulkarni |
| Autore | Kulkarni Parag |
| Pubbl/distr/stampa | Hoboken [New Jersey] : , : John Wiley & Sons, , c2012 |
| Descrizione fisica | 1 online resource (311 p.) |
| Disciplina |
006.3/1
006.31 |
| Collana | IEEE Press Series on Systems Science and Engineering |
| Soggetto topico |
Reinforcement learning
Machine learning Decision making |
| ISBN |
1-282-13449-3
9786613807076 1-118-27155-6 1-118-27153-X 1-118-26650-1 |
| Classificazione | TEC008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xv -- Acknowledgments xix -- About the Author xxi -- 1 Introduction to Reinforcement and Systemic Machine Learning 1 -- 1.1. Introduction 1 -- 1.2. Supervised, Unsupervised, and Semisupervised Machine Learning 2 -- 1.3. Traditional Learning Methods and History of Machine Learning 4 -- 1.4. What Is Machine Learning? 7 -- 1.5. Machine-Learning Problem 8 -- 1.6. Learning Paradigms 9 -- 1.7. Machine-Learning Techniques and Paradigms 12 -- 1.8. What Is Reinforcement Learning? 14 -- 1.9. Reinforcement Function and Environment Function 16 -- 1.10. Need of Reinforcement Learning 17 -- 1.11. Reinforcement Learning and Machine Intelligence 17 -- 1.12. What Is Systemic Learning? 18 -- 1.13. What Is Systemic Machine Learning? 18 -- 1.14. Challenges in Systemic Machine Learning 19 -- 1.15. Reinforcement Machine Learning and Systemic Machine Learning 19 -- 1.16. Case Study Problem Detection in a Vehicle 20 -- 1.17. Summary 20 -- 2 Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning 23 -- 2.1. Introduction 23 -- 2.2. What Is Systemic Machine Learning? 27 -- 2.3. Generalized Systemic Machine-Learning Framework 30 -- 2.4. Multiperspective Decision Making and Multiperspective Learning 33 -- 2.5. Dynamic and Interactive Decision Making 43 -- 2.6. The Systemic Learning Framework 47 -- 2.7. System Analysis 52 -- 2.8. Case Study: Need of Systemic Learning in the Hospitality Industry 54 -- 2.9. Summary 55 -- 3 Reinforcement Learning 57 -- 3.1. Introduction 57 -- 3.2. Learning Agents 60 -- 3.3. Returns and Reward Calculations 62 -- 3.4. Reinforcement Learning and Adaptive Control 63 -- 3.5. Dynamic Systems 66 -- 3.6. Reinforcement Learning and Control 68 -- 3.7. Markov Property and Markov Decision Process 68 -- 3.8. Value Functions 69 -- 3.8.1. Action and Value 70 -- 3.9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 70 -- 3.10. Dynamic Programming 71 -- 3.11. Adaptive Dynamic Programming 71 -- 3.12. Example: Reinforcement Learning for Boxing Trainer 75.
3.13. Summary 75 -- 4 Systemic Machine Learning and Model 77 -- 4.1. Introduction 77 -- 4.2. A Framework for Systemic Learning 78 -- 4.3. Capturing the Systemic View 86 -- 4.4. Mathematical Representation of System Interactions 89 -- 4.5. Impact Function 91 -- 4.6. Decision-Impact Analysis 91 -- 4.7. Summary 97 -- 5 Inference and Information Integration 99 -- 5.1. Introduction 99 -- 5.2. Inference Mechanisms and Need 101 -- 5.3. Integration of Context and Inference 107 -- 5.4. Statistical Inference and Induction 111 -- 5.5. Pure Likelihood Approach 112 -- 5.6. Bayesian Paradigm and Inference 113 -- 5.7. Time-Based Inference 114 -- 5.8. Inference to Build a System View 114 -- 5.9. Summary 118 -- 6 Adaptive Learning 119 -- 6.1. Introduction 119 -- 6.2. Adaptive Learning and Adaptive Systems 119 -- 6.3. What Is Adaptive Machine Learning? 123 -- 6.4. Adaptation and Learning Method Selection Based on Scenario 124 -- 6.5. Systemic Learning and Adaptive Learning 127 -- 6.6. Competitive Learning and Adaptive Learning 140 -- 6.7. Examples 146 -- 6.8. Summary 149 -- 7 Multiperspective and Whole-System Learning 151 -- 7.1. Introduction 151 -- 7.2. Multiperspective Context Building 152 -- 7.3. Multiperspective Decision Making and Multiperspective Learning 154 -- 7.4. Whole-System Learning and Multiperspective Approaches 164 -- 7.5. Case Study Based on Multiperspective Approach 167 -- 7.6. Limitations to a Multiperspective Approach 174 -- 7.7. Summary 174 -- 8 Incremental Learning and Knowledge Representation 177 -- 8.1. Introduction 177 -- 8.2. Why Incremental Learning? 178 -- 8.3. Learning from What Is Already Learned. . . 180 -- 8.4. Supervised Incremental Learning 191 -- 8.5. Incremental Unsupervised Learning and Incremental Clustering 191 -- 8.6. Semisupervised Incremental Learning 196 -- 8.7. Incremental and Systemic Learning 199 -- 8.8. Incremental Closeness Value and Learning Method 200 -- 8.9. Learning and Decision-Making Model 205 -- 8.10. Incremental Classification Techniques 206. 8.11. Case Study: Incremental Document Classification 207 -- 8.12. Summary 208 -- 9 Knowledge Augmentation: A Machine Learning Perspective 209 -- 9.1. Introduction 209 -- 9.2. Brief History and Related Work 211 -- 9.3. Knowledge Augmentation and Knowledge Elicitation 215 -- 9.4. Life Cycle of Knowledge 217 -- 9.5. Incremental Knowledge Representation 222 -- 9.6. Case-Based Learning and Learning with Reference to Knowledge Loss 224 -- 9.7. Knowledge Augmentation: Techniques and Methods 224 -- 9.8. Heuristic Learning 228 -- 9.9. Systemic Machine Learning and Knowledge Augmentation 229 -- 9.10. Knowledge Augmentation in Complex Learning Scenarios 232 -- 9.11. Case Studies 232 -- 9.12. Summary 235 -- 10 Building a Learning System 237 -- 10.1. Introduction 237 -- 10.2. Systemic Learning System 237 -- 10.3. Algorithm Selection 242 -- 10.4. Knowledge Representation 244 -- 10.5. Designing a Learning System 245 -- 10.6. Making System to Behave Intelligently 246 -- 10.7. Example-Based Learning 246 -- 10.8. Holistic Knowledge Framework and Use of Reinforcement Learning 246 -- 10.9. Intelligent Agents-Deployment and Knowledge Acquisition and Reuse 250 -- 10.10. Case-Based Learning: Human Emotion-Detection System 251 -- 10.11. Holistic View in Complex Decision Problem 253 -- 10.12. Knowledge Representation and Data Discovery 255 -- 10.13. Components 258 -- 10.14. Future of Learning Systems and Intelligent Systems 259 -- 10.15. Summary 259 -- Appendix A: Statistical Learning Methods 261 -- Appendix B: Markov Processes 271 -- Index 281. |
| Record Nr. | UNINA-9910139690703321 |
|
Kulkarni Parag
|
||
| Hoboken [New Jersey] : , : John Wiley & Sons, , c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reinforcement and systemic machine learning for decision making / / Parag Kulkarni
| Reinforcement and systemic machine learning for decision making / / Parag Kulkarni |
| Autore | Kulkarni Parag |
| Descrizione fisica | 1 online resource (312 pages) |
| Disciplina |
006.3/1
006.31 |
| Collana | IEEE Press Series on Systems Science and Engineering |
| Soggetto topico |
Reinforcement learning
Machine learning Decision making |
| ISBN |
1-282-13449-3
9786613807076 1-118-27155-6 1-118-27153-X 1-118-26650-1 |
| Classificazione | TEC008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Preface xv -- Acknowledgments xix -- About the Author xxi -- 1 Introduction to Reinforcement and Systemic Machine Learning 1 -- 1.1. Introduction 1 -- 1.2. Supervised, Unsupervised, and Semisupervised Machine Learning 2 -- 1.3. Traditional Learning Methods and History of Machine Learning 4 -- 1.4. What Is Machine Learning? 7 -- 1.5. Machine-Learning Problem 8 -- 1.6. Learning Paradigms 9 -- 1.7. Machine-Learning Techniques and Paradigms 12 -- 1.8. What Is Reinforcement Learning? 14 -- 1.9. Reinforcement Function and Environment Function 16 -- 1.10. Need of Reinforcement Learning 17 -- 1.11. Reinforcement Learning and Machine Intelligence 17 -- 1.12. What Is Systemic Learning? 18 -- 1.13. What Is Systemic Machine Learning? 18 -- 1.14. Challenges in Systemic Machine Learning 19 -- 1.15. Reinforcement Machine Learning and Systemic Machine Learning 19 -- 1.16. Case Study Problem Detection in a Vehicle 20 -- 1.17. Summary 20 -- 2 Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning 23 -- 2.1. Introduction 23 -- 2.2. What Is Systemic Machine Learning? 27 -- 2.3. Generalized Systemic Machine-Learning Framework 30 -- 2.4. Multiperspective Decision Making and Multiperspective Learning 33 -- 2.5. Dynamic and Interactive Decision Making 43 -- 2.6. The Systemic Learning Framework 47 -- 2.7. System Analysis 52 -- 2.8. Case Study: Need of Systemic Learning in the Hospitality Industry 54 -- 2.9. Summary 55 -- 3 Reinforcement Learning 57 -- 3.1. Introduction 57 -- 3.2. Learning Agents 60 -- 3.3. Returns and Reward Calculations 62 -- 3.4. Reinforcement Learning and Adaptive Control 63 -- 3.5. Dynamic Systems 66 -- 3.6. Reinforcement Learning and Control 68 -- 3.7. Markov Property and Markov Decision Process 68 -- 3.8. Value Functions 69 -- 3.8.1. Action and Value 70 -- 3.9. Learning an Optimal Policy (Model-Based and Model-Free Methods) 70 -- 3.10. Dynamic Programming 71 -- 3.11. Adaptive Dynamic Programming 71 -- 3.12. Example: Reinforcement Learning for Boxing Trainer 75.
3.13. Summary 75 -- 4 Systemic Machine Learning and Model 77 -- 4.1. Introduction 77 -- 4.2. A Framework for Systemic Learning 78 -- 4.3. Capturing the Systemic View 86 -- 4.4. Mathematical Representation of System Interactions 89 -- 4.5. Impact Function 91 -- 4.6. Decision-Impact Analysis 91 -- 4.7. Summary 97 -- 5 Inference and Information Integration 99 -- 5.1. Introduction 99 -- 5.2. Inference Mechanisms and Need 101 -- 5.3. Integration of Context and Inference 107 -- 5.4. Statistical Inference and Induction 111 -- 5.5. Pure Likelihood Approach 112 -- 5.6. Bayesian Paradigm and Inference 113 -- 5.7. Time-Based Inference 114 -- 5.8. Inference to Build a System View 114 -- 5.9. Summary 118 -- 6 Adaptive Learning 119 -- 6.1. Introduction 119 -- 6.2. Adaptive Learning and Adaptive Systems 119 -- 6.3. What Is Adaptive Machine Learning? 123 -- 6.4. Adaptation and Learning Method Selection Based on Scenario 124 -- 6.5. Systemic Learning and Adaptive Learning 127 -- 6.6. Competitive Learning and Adaptive Learning 140 -- 6.7. Examples 146 -- 6.8. Summary 149 -- 7 Multiperspective and Whole-System Learning 151 -- 7.1. Introduction 151 -- 7.2. Multiperspective Context Building 152 -- 7.3. Multiperspective Decision Making and Multiperspective Learning 154 -- 7.4. Whole-System Learning and Multiperspective Approaches 164 -- 7.5. Case Study Based on Multiperspective Approach 167 -- 7.6. Limitations to a Multiperspective Approach 174 -- 7.7. Summary 174 -- 8 Incremental Learning and Knowledge Representation 177 -- 8.1. Introduction 177 -- 8.2. Why Incremental Learning? 178 -- 8.3. Learning from What Is Already Learned. . . 180 -- 8.4. Supervised Incremental Learning 191 -- 8.5. Incremental Unsupervised Learning and Incremental Clustering 191 -- 8.6. Semisupervised Incremental Learning 196 -- 8.7. Incremental and Systemic Learning 199 -- 8.8. Incremental Closeness Value and Learning Method 200 -- 8.9. Learning and Decision-Making Model 205 -- 8.10. Incremental Classification Techniques 206. 8.11. Case Study: Incremental Document Classification 207 -- 8.12. Summary 208 -- 9 Knowledge Augmentation: A Machine Learning Perspective 209 -- 9.1. Introduction 209 -- 9.2. Brief History and Related Work 211 -- 9.3. Knowledge Augmentation and Knowledge Elicitation 215 -- 9.4. Life Cycle of Knowledge 217 -- 9.5. Incremental Knowledge Representation 222 -- 9.6. Case-Based Learning and Learning with Reference to Knowledge Loss 224 -- 9.7. Knowledge Augmentation: Techniques and Methods 224 -- 9.8. Heuristic Learning 228 -- 9.9. Systemic Machine Learning and Knowledge Augmentation 229 -- 9.10. Knowledge Augmentation in Complex Learning Scenarios 232 -- 9.11. Case Studies 232 -- 9.12. Summary 235 -- 10 Building a Learning System 237 -- 10.1. Introduction 237 -- 10.2. Systemic Learning System 237 -- 10.3. Algorithm Selection 242 -- 10.4. Knowledge Representation 244 -- 10.5. Designing a Learning System 245 -- 10.6. Making System to Behave Intelligently 246 -- 10.7. Example-Based Learning 246 -- 10.8. Holistic Knowledge Framework and Use of Reinforcement Learning 246 -- 10.9. Intelligent Agents-Deployment and Knowledge Acquisition and Reuse 250 -- 10.10. Case-Based Learning: Human Emotion-Detection System 251 -- 10.11. Holistic View in Complex Decision Problem 253 -- 10.12. Knowledge Representation and Data Discovery 255 -- 10.13. Components 258 -- 10.14. Future of Learning Systems and Intelligent Systems 259 -- 10.15. Summary 259 -- Appendix A: Statistical Learning Methods 261 -- Appendix B: Markov Processes 271 -- Index 281. |
| Record Nr. | UNINA-9910831085703321 |
|
Kulkarni Parag
|
||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Reverse Hypothesis Machine Learning : A Practitioner's Perspective / / by Parag Kulkarni
| Reverse Hypothesis Machine Learning : A Practitioner's Perspective / / by Parag Kulkarni |
| Autore | Kulkarni Parag |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XVI, 138 p. 61 illus., 9 illus. in color.) |
| Disciplina | 006.31 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computational intelligence
Knowledge management Machinery Management Industrial management Electronics Microelectronics Computational Intelligence Knowledge Management Machinery and Machine Elements Innovation/Technology Management Electronics and Microelectronics, Instrumentation |
| ISBN | 3-319-55312-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Pattern Apart -- Understanding Machine Learning Opportunities -- Systemic Machine Learning -- Reinforcement and Deep Reinforcement Machine Learning -- Creative Machine Learning -- Co-operative and Collective learning for Creative Machine Learning -- Building Creative Machines with Optimal Machine Learning and Creative Machine Learning Applications -- Conclusion – Learning Continues. |
| Record Nr. | UNINA-9910254341403321 |
|
Kulkarni Parag
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||