Explanation Based Neural Network Learning

Author: Sebastian Thrun
Publisher: Springer Science & Business Media
ISBN: 9781461313816
Size: 19.87 MB
Format: PDF, Kindle
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Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.

Neural Symbolic Cognitive Reasoning

Author: Artur S. D'Avila Garcez
Publisher: Springer Science & Business Media
ISBN: 9783540732457
Size: 18.19 MB
Format: PDF, ePub
View: 98

This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.

The Logic Of Adaptive Behavior

Author: M. Van
Publisher: IOS Press
ISBN: 9781586039691
Size: 11.43 MB
Format: PDF
View: 61

Learning and reasoning in large, structured, probabilistic worlds is at the heart of artificial intelligence. Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. Many efficient reinforcement learning and dynamic programming techniques exist that can solve such problems. Until recently, the representational state-of-the-art in this field was based on propositional representations.

Machine Learning

Author: Source Wikipedia
Publisher: University-Press.org
ISBN: 1230625151
Size: 13.98 MB
Format: PDF, ePub
View: 82

Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 254. Chapters: Artificial neural network, Supervised learning, Hidden Markov model, Pattern recognition, Reinforcement learning, Principal component analysis, Self-organizing map, Overfitting, Cluster analysis, Granular computing, Rough set, Mixture model, Expectation-maximization algorithm, Radial basis function network, Types of artificial neural networks, Learning to rank, Forward-backward algorithm, Perceptron, Category utility, Neural modeling fields, Dominance-based rough set approach, Principle of maximum entropy, Non-negative matrix factorization, Concept learning, K-means clustering, Structure mapping engine, Viterbi algorithm, Cross-validation, Hierarchical temporal memory, Activity recognition, Algorithmic inference, Formal concept analysis, Gradient boosting, Information bottleneck method, Nearest neighbor search, Simultaneous localization and mapping, Markov decision process, Gittins index, K-nearest neighbor algorithm, General Architecture for Text Engineering, Reasoning system, Concept drift, Uniform convergence, Conceptual clustering, Multi-armed bandit, Multilinear subspace learning, Conditional random field, DBSCAN, Feature selection, Learning with errors, Weka, Evolutionary algorithm, Iris flower data set, Binary classification, OPTICS algorithm, Partially observable Markov decision process, Constrained Conditional Models, Group method of data handling, Learning classifier system, Random forest, Statistical classification, Analogical modeling, Bregman divergence, Backpropagation, Temporal difference learning, Loss function, Curse of dimensionality, Alternating decision tree, Evolutionary multi-modal optimization, Stochastic gradient descent, Kernel principal component analysis, Explanation-based learning, K-medoids, RapidMiner, Transduction, Variable-order Markov model, Kernel adaptive filter, Classification...

Fundamentals Of Artificial Neural Networks

Author: Mohamad H. Hassoun
Publisher: MIT Press
ISBN: 026208239X
Size: 15.54 MB
Format: PDF, Docs
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Fundamentals of Building Energy Dynamics assesses how and why buildings use energy, and how energy use and peak demand can be reduced. It provides a basis for integrating energy efficiency and solar approaches in ways that will allow building owners and designers to balance the need to minimize initial costs, operating costs, and life-cycle costs with need to maintain reliable building operations and enhance environmental quality both inside and outside the building. Chapters trace the development of building energy systems and analyze the demand side of solar applications as a means for determining what portion of a building's energy requirements can potentially be met by solar energy.Following the introduction, the book provides an overview of energy use patterns in the aggregate U.S. building population. Chapter 3 surveys work on the energy flows in an individual building and shows how these flows interact to influence overall energy use. Chapter 4 presents the analytical methods, techniques, and tools developed to calculate and analyze energy use in buildings, while chapter 5 provides an extensive survey of the energy conservation and management strategies developed in the post-energy crisis period.The approach taken is a commonsensical one, starting with the proposition that the purpose of buildings is to house human activities, and that conservation measures that negatively affect such activities are based on false economies. The goal is to determine rational strategies for the design of new buildings, and the retrofit of existing buildings to bring them up to modern standards of energy use. The energy flows examined are both large scale (heating systems) and small scale (choices among appliances).Solar Heat Technologies: Fundamentals and Applications, Volume 4