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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.
- Sales Rank: #10515387 in Books
- Brand: Brand: IGI Global
- Published on: 2012-07-31
- Original language: English
- Number of items: 1
- Dimensions: 11.02" h x .75" w x 8.50" l, 2.35 pounds
- Binding: Hardcover
- 367 pages
Features
- Used Book in Good Condition
Review
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. --Annotation ©2012 Book News Inc. Portland, OR
About the Author
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 I. 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.
Most helpful customer reviews
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A book of collected stories on Logic in Machine Learning
By Machine
This is a book of, so to speak, collected stories mainly on Logic in Machine Learning (both theory and practise) written by different talented authors. Some of them did their studies in USSR and this is a first time when their results (recent and past) comprise a part of a book in English. E.g. one of them is a USSR pioneer of Cybernetics (e.g. project URAL-1), outstanding Prof. Arkady Zakrevsky (or Arkadij Zakrevskij). He invented the LYaPAS language and algorithms for discrete automata synthesis at that time (see LYaPAS: A programming language for logic and coding algorithms). Now he is a quite active person and contributed two papers on Inductyive-Deductive Inference and Implicative Regularities as well as on Solving Large Systems of Boolean Equations (Logic for Big Data as I could say). Another author and editor is Xenia Naidenova, she works on the topics of mining logical rules from data and diagnostics test in Machine Lerning. It is interesting, e.g. that so called association rules, introduced by R. Agrawal, were known even earlier, e.g. in Formal Concept Analysis they were known as partial implications (see papers of M. Luxenburger) at the end of 80s. Xenia also proposed similar approcahes at the end of 80s in USSR, also before Agrawal. It is interesting to see these results and their contribution to Machine Learning area in her survey. Another interesting paper describes so called bimodal cross-validation; this approach extends standard cross-validation technique in Machine Learning to the case of Recommender Systems (one can find this in somewhat similar to perplexity measure in Topic Modeling). Other interesting papers covers the topics on Logical Inferece and Defesiable Inference in a specially designed N-tuple algebra, Machine Learning approaches for synthesis new inorganic compounds, ML in medical treatment, Bussiness Intelligence for E-covernment and even measuring a human intelligence by soft computing techniques. I recommend the book for those who is interested both in Logic and Machine Learing as well as in their applications. Of course it is not a textbook and some familiarity with basic Logic and Machine Learning ideas and notions is required.
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