Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models, 1st Edition

  • Xenia Naidenova
  • Published By:
  • ISBN-10: 1605668117
  • ISBN-13: 9781605668116
  • DDC: 006.3
  • Grade Level Range: College Freshman - College Senior
  • 312 Pages | eBook
  • Original Copyright 2009 | Published/Released June 2010
  • This publication's content originally published in print form: 2009

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About

Overview

The reduction of machine learning algorithms to commonsense reasoning processes is now possible due to the reformulation of machine learning problems as searching the best approximation of a given classification on a given set of examples."Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models" provides a unique view on classification as a key to human commonsense reasoning and transforms traditional considerations of data and knowledge communications. Containing leading research evolved from international investigations, this book presents an effective classification of logical rules used in the modeling of commonsense reasoning.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Table of Contents.
Preface.
Acknowledgment.
1: Knowledge in the Psychology of Thinking and Mathematics.
2: Logic-Based Reasoning in the Framework of Artificial Intelligence.
3: The Coordination of Commonsense Reasoning Operations.
4: The Logical Rules of Commonsense Reasoning.
5: The Examples of Human Commonsense Reasoning Processes.
6: Machine Learning (ML) as a Diagnostic Task.
7: The Concept of Good Classification (Diagnostic) Test.
8: The Duality of Good Diagnostic Tests.
9: Towards an Integrative Model of Deductive–Inductive Commonsense Reasoning.
10: Towards a Model of Fuzzy Commonsense Reasoning.
11: Object-Oriented Technology for Expert System Generation.
12: Case Technology for Psycho-Diagnostic System Generation.
13: Commonsense Reasoning in Intelligent Computer Systems.
Conclusion.
About the Author.
Index.