Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: โข Methods for classification, regression, and recommendations โข So
Machine Learning Methods for Planning
- Publisher
- Elsevier Inc
- Year
- 1993
- Tongue
- English
- Leaves
- 544
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
Content:
The Morgan Kaufmann Series in Machine Learning, Page ii
Front Matter, Page iii
Copyright, Page iv
Contributors, Pages vii-viii
Preface, Pages ix-xiii, STEVEN MINTON
CHAPTER 1 - Learning, Planning, and Scheduling: An Overview, Pages 1-29, STEVEN MINTON, MONTE ZWEBEN
CHAPTER 2 - Interfaces That Learn: A Learning Apprentice for Calendar Management, Pages 31-65, JEAN JOURDAN, LISA DENT, JOHN MCDERMOTT, TOM MITCHELL, DAVID ZABOWSKI
CHAPTER 3 - Reinforcement Learning for Planning and Control, Pages 67-92, THOMAS DEAN, KEN BASYE, JOHN SHEWCHUK
CHAPTER 4 - A First Theory of Plausible Inference and Its Use in Continuous Domain Planning, Pages 93-124, GERALD DEJONG, DANIEL OBLINGER
CHAPTER 5 - Planning, Acting, and Learning in a Dynamic Domain, Pages 125-158, ALBERTO SEGRE, JENNIFER TURNEY
CHAPTER 6 - Reactive, Integrated Systems Pose New Problems for Machine Learning, Pages 159-195, JOHN BRESINA, MARK DRUMMOND, SMADAR KEDAR
CHAPTER 7 - Bias in Planning and Explanation-Based Learning, Pages 197-232, PAUL S. ROSENBLOOM, SOOWON LEE, AMY UNRUH
CHAPTER 8 - Toward Scaling Up Machine Learning: A Case Study with Derivational Analogy in PRODIGY, Pages 233-272, MANUELA M. VELOSO, JAIME G. CARBONELL
CHAPTER 9 - Integration of Analogical Search Control and Explanation-Based Learning of Correctness, Pages 273-315, KURT VANLEHN, RANDOLPH M. JONES
CHAPTER 10 - A Unified Framework for Planning and Learning, Pages 317-350, PAT LANGLEY, JOHN A. ALLEN
CHAPTER 11 - Toward a Theory of Agency, Pages 351-396, KRISTIAN HAMMOND, TIMOTHY CONVERSE, MITCHELL MARKS
CHAPTER 12 - Supporting Flexible Plan Reuse, Pages 397-434, SUBBARAO KAMBHAMPATI
CHAPTER 13 - Adapting Plan Architectures, Pages 435-466, WILLIAM S. MARK
CHAPTER 14 - Learning Recurring Subplans, Pages 467-497, DAVID RUBY, DENNIS KIBLER
CHAPTER 15 - A Method for Biasing the Learning of Nonterminal Reduction Rules, Pages 499-535, STACY C. MARSELLA, CHARLES F. SCHMIDT
Index, Pages 537-540
๐ SIMILAR VOLUMES
InEnsemble Methods for Machine Learning you'll learn to implement the most important ensemble machine learning methods from scratch. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning m
<p>It is difficult to become an ecologist withou,t acquiring some breadth~ For example, we are expected to be competent statisticians and taxonomists who appreciate the importance of spatial and temporal processes, whilst recognising the potential offered by techniques such as RAPD. It is, therefore
<span>This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-