Reverse hypothesis machine learning : practitioner's perspective
This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since unde...
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| Main Author: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cham, Switzerland :
Springer,
2017.
|
| Series: | Intelligent systems reference library ;
v. 128. |
| Subjects: | |
| ISBN: | 9783319553122 9783319553115 |
| Physical Description: | 1 online resource : illustrations |
Cover
Table of contents
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| 100 | 1 | |a Kulkarni, Parag, |e author. | |
| 245 | 1 | 0 | |a Reverse hypothesis machine learning : |b practitioner's perspective / |c Parag Kulkarni. |
| 264 | 1 | |a Cham, Switzerland : |b Springer, |c 2017. | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a počítač |b c |2 rdamedia | ||
| 338 | |a online zdroj |b cr |2 rdacarrier | ||
| 490 | 1 | |a Intelligent systems reference library ; |v Volume 128 | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Acknowledgements; Author's Note; Contents; About the Author; Building Foundation: Decoding Knowledge Acquisition; 1 Introduction: Patterns Apart; 1.1 A Naked World of Data Warriors!; 1.2 Introduction-The Blind Data Game; 1.3 Putting Creativity on Weak Legs: Can We Make Present Machines Creative?; 1.4 Learning Using Creative Models; 1.5 Plundered Every Data Point-Data Rich Knowledge Poor Society; 1.6 Computational Creativity and Data Analysis; 1.7 Simple Paradigms and Evaluations: (Machine Learning Compass and Barometer). | |
| 505 | 8 | |a 1.8 After All Its Time for Knowledge Innovation-Do not just Build Innovate1.9 What Is Knowledge Innovation? (Meta-Knowledge Approach); 1.10 Knowledge Innovation Model Building; 1.11 Creative Intelligence to Collective Knowledge Innovation: (Intelligible Togetherness); 1.12 Do not Dive Deep Unnecessarily: (Your Machine Learning Life Guard in Deep Data Sea); 1.13 Machine Learning and Knowledge Innovation; 1.14 Making Intelligent Agent Intelligent; 1.15 Architecting Intelligence; 1.16 Summary; 2 Understanding Machine Learning Opportunities. | |
| 505 | 8 | |a 2.1 Understanding Learning Opportunity (Catching Data Signals Right)2.2 Knowledge Innovation Building Blocks of ML and Intelligent Systems; 2.3 Stages in Limited Exploration; 2.4 Mathematical Equations for Classification; 2.5 New Paradigms in This Book; 2.6 iknowlation's IDEA Matrix for Machine Learning Opportunity Evaluation; 2.7 Using IDEA Matrix to Identify ML Opportunity; 2.8 Self-evaluation of Learning; 2.9 Mathematical Model of Learnability; 2.10 Building Machine Learning Models: Your Foundation for Surprising Solutions; 2.11 Opportunity Cycle; 2.12 ML Big Landscape. | |
| 505 | 8 | |a 2.13 Context-Based Learning-Respect Heterogeneity2.14 Summary; 3 Systemic Machine Learning; 3.1 What Is a System? (Decoding Connectivity); 3.2 What Is Systemic Machine Learning: (Exploiting Togetherness); 3.3 Systemic Machine Learning Model and Algorithm Selection; 3.4 Cognitive Systemic Machine Learning Models; 3.5 Cognitive Interaction Centric Models; 3.6 Meta-Reasoning Centric Models (System of System); 3.6.1 System Study; 3.6.2 Learning with Limited Data; 3.7 Summary; 4 Reinforcement and Deep Reinforcement Machine Learning; 4.1 Introduction; 4.2 Reinforcement Learning; 4.3 Learning Agents. | |
| 505 | 8 | |a 4.4 Returns and Reward Calculations (Evaluate Your Position and Actions)4.5 Dynamic Systems (Making Best Use of Unpredictability); 4.6 Dynamic Environment and Dynamic System; 4.7 Reinforcement Learning and Exploration; 4.8 Markov Property and Markov Decision Process; 4.9 Value Functions; 4.10 Action and Value; 4.11 Learning an Optimal Policy (Model-Based and Model-Free Methods); 4.12 Uncertainty; 4.13 Adaptive Dynamic Learning (Learning Evolution); 4.14 Temporal Difference (TD) Learning; 4.15 Q Learning; 4.16 Unified View; 4.17 Deep Exploratory Machine Learning; 4.18 Summary. | |
| 506 | |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty | ||
| 520 | |a This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same--the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity. | ||
| 590 | |a SpringerLink |b Springer Complete eBooks | ||
| 650 | 0 | |a Machine learning. | |
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| 655 | 9 | |a electronic books |2 eczenas | |
| 776 | 0 | 8 | |i Print version: |a Kulkarni, Parag. |t Reverse Hypothesis Machine Learning : A Practitioner's Perspective. |d Cham : Springer International Publishing, ©2017 |z 9783319553115 |
| 830 | 0 | |a Intelligent systems reference library ; |v v. 128. | |
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