Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models

Machine Learning Methods for Commonsense Reasoning Processes: Interactive Models

Xenia Naidenova (Military Medical Academy, Russia)
Indexed In: SCOPUS
Release Date: October, 2009|Copyright: © 2010 |Pages: 424
DOI: 10.4018/978-1-60566-810-9
ISBN13: 9781605668109|ISBN10: 1605668109|EISBN13: 9781605668116
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Description & Coverage
Description:

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.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Artificial Intelligence
  • Commonsense reasoning
  • Coordination of commonsense reasoning operations
  • Deductive-inductive commonsense reasoning
  • Expert system generation
  • Human commonsense reasoning processes
  • Knowledge in the psychology of thinking
  • Machine Learning
  • Modeling conceptual reasoning
  • Object-oriented technology
  • Psycho-diagnostic systems generation
  • Reasoning in intelligent computer systems
Reviews & Statements

This book demonstrates the possibility of transforming a large class of machine learning algorithms into integrated commonsense reasoning processes in which inductive and deductive inferences are not separated one from another but moreover they are correlated and support one another.

– Xenia Naidenova, Military Medical Academy, Russia
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Editor/Author Biographies
Xenia Naidenova is a senior researcher of the Group of Psycho Diagnostic Systems’ Automation at the Military Medical Academy (St. Petersburg, Russia). She is currently the head of Project DIALOG: Methods of Data Mining in Psychological and Physiological Diagnostics. Dr. Naidenova received a diploma of engineering with a specialty in computer engineering (1963) and a PhD in technical sciences (1979), both from the Lenin Electro-Technical Institute of Leningrad. In 1999 she received a senior researcher diploma from the Military Medical Academy (St. Petersburg, Russia). She has guided the development of several program systems on knowledge acquisition and machine learning including DEFINE, SIZIF, CLAST, LAD, and diagnostic test machines and has published over 150 papers. Dr. Naidenova is a member of the Russian Association for Artificial Intelligence and is on the Program Committee for the KDS.
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