Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems

Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems

Xenia Naidenova, Dmitry I. Ignatov
Indexed In: SCOPUS View 1 More Indices
Release Date: July, 2012|Copyright: © 2013 |Pages: 367
DOI: 10.4018/978-1-4666-1900-5
ISBN13: 9781466619005|ISBN10: 1466619007|EISBN13: 9781466619012
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Description & Coverage
Description:

The consideration of symbolic machine learning algorithms as an entire class will make it possible, in the future, to generate algorithms, with the aid of some parameters, depending on the initial users' requirements and the quality of solving targeted problems in domain applications.

Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems surveys, analyzes, and compares the most effective algorithms for mining all kinds of logical rules. Global academics and professionals in related fields have come together to create this unique knowledge-sharing resources which will serve as a forum for future collaborations.

Coverage:

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

  • Algorithms
  • Apriori-Like Bottom-Up Search
  • Closure Operations of Galois Connections
  • Data Mining
  • Diagnostic Test Approach
  • Formal Concept Analysis
  • Knowledge Discovery
  • Machine Learning
  • Ontologies
  • Web Mining
Reviews & Statements

Taking commonsense reasoning as a process of thinking that reveals causal connections between objects, their properties, and their classes, mathematicians and computer scientists — most of the them Russian — explore the role it can play in machine learning and intelligent computer systems. After setting out theoretical models of logical inference, they explore some new and original direction in artificial intelligence, machine learning, Internet data analysis, and creating intelligent computer systems. Then they demonstration applications of machine learning, knowledge elicitation, and knowledge organization in different problem domains, among them predicting new inorganic compounds and their properties, evaluating the organism's functional state of individuals depending on their immune reactivity, and business intelligence in corporate governance. 

– Book News Inc. Portland, OR
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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.
Dr. Dmitry Ignatov works as an Assistant Professor for National Research University Higher School of Economics (Moscow, Russia) at the chair of Artificial Intelligence and Data Analysis. Dr. Dmitry Ignatov graduated in 2004 as a “Specialist in Physics and Mathematics” with distinction at the “Kolomna Teachers' Training Institute” (Russia, Kolomna) and in 2008 as a "Master of Applied Mathematics and Information Sciences" at the "State University Higher School of Economics" (Russia, Moscow). In 2010 he obtained his degree of “Candidate of sciences in Mathematical Modeling, Numerical Methods, and Software Systems” at the “National Research University Higher School of Economics”. He did his PhD (Candidate of science in Russian) research in All-Russian Institute for Scientific and Technical Information specializing in Theoretical Computer Science. He also was a guest researcher as a PhD student of the Postgraduate Program "Specification of Discrete Processes and Systems of Processes by Operational Models and Logics", Department of Computer Science, Dresden University of Technology. He is an author of more than 35 papers published in peer reviewed conferences, workshops and journals. His main interests include Formal Concept Analysis, Data Mining and Machine Learning, especially multimodal clustering and recommender systmes. He was a co-organizer of several international conferences and workshops: ICCS 2009, RSFDGrC 2011, PReMI 2011, CDUD 2011 and 2012, SCAKD 2011, EEML 2012, ICFCA 2012.
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