Michael J. Pazzani – författare

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6 produkter

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6 produkter

Creating A Memory of Causal Relationships
An Integration of Empirical and Explanation-based Learning Methods
AvMichael J. Pazzani
Inbunden, Engelska, 1990
723 kr
Skickas inom 10-15 vardagar
This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.
Creating A Memory of Causal Relationships
An Integration of Empirical and Explanation-based Learning Methods
AvMichael J. Pazzani
Häftad, Engelska, 2016
778 kr
Skickas inom 10-15 vardagar
This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.
Creating A Memory of Causal Relationships
An Integration of Empirical and Explanation-based Learning Methods
AvMichael J. Pazzani
E-bok
Engelska, 2014
913 kr
Läs direkt efter köp
This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.
Creating A Memory of Causal Relationships
An Integration of Empirical and Explanation-based Learning Methods
AvMichael J. Pazzani
E-bok
PDF, Engelska, 2014
913 kr
Läs direkt efter köp
This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.
Concept Formation
Knowledge and Experience in Unsupervised Learning
AvPat Langley,Michael J. Pazzanim. fl.
E-bok
PDF, Engelska, 2014
781 kr
Läs direkt efter köp
Concept Formation: Knowledge and Experience in Unsupervised Learning presents the interdisciplinary interaction between machine learning and cognitive psychology on unsupervised incremental methods. This book focuses on measures of similarity, strategies for robust incremental learning, and the psychological consistency of various approaches. Organized into three parts encompassing 15 chapters, this book begins with an overview of inductive concept learning in machine learning and psychology, with emphasis on issues that distinguish concept formation from more prevalent supervised methods and from numeric and conceptual clustering. This text then describes the cognitive consistency of two concept formation systems that are motivated by a rational analysis of human behavior relative to a variety of psychological phenomena. Other chapters consider the merits of various schemes for representing and acquiring knowledge during concept formation. This book discusses as well the earliest work in concept formation. The final chapter deals with acquisition of quantity conservation in developmental psychology. This book is a valuable resource for psychologists and cognitive scientists.
Creating A Memory of Causal Relationships
An Integration of Empirical and Explanation-based Learning Methods
AvMichael J. Pazzani
Häftad, Engelska, 1990
596 kr
Tillfälligt slut
This book presents a theory of learning new causal relationships by making use of perceived regularities in the environment, general knowledge of causality, and existing causal knowledge. Integrating ideas from the psychology of causation and machine learning, the author introduces a new learning procedure called theory-driven learning that uses abstract knowledge of causality to guide the induction process. Known as OCCAM, the system uses theory-driven learning when new experiences conform to common patterns of causal relationships, empirical learning to learn from novel experiences, and explanation-based learning when there is sufficient existing knowledge to explain why a new outcome occurred. Together these learning methods construct a hierarchical organized memory of causal relationships. As such, OCCAM is the first learning system with the ability to acquire, via empirical learning, the background knowledge required for explanation-based learning. Please note: This program runs on common lisp.

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Michael J. Pazzani – författare

Creating A Memory of Causal Relationships

Creating A Memory of Causal Relationships

Creating A Memory of Causal Relationships

Creating A Memory of Causal Relationships

Concept Formation

Creating A Memory of Causal Relationships

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