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3. Textbook for a course in expert systems, if an emphasis is
placed on Chapters 1 to 3 and on a selection of material from
Chapters 4 to 7. There is also the option of using an additional
commercially available sheU for a programming project. In assigning
a programming project, the instructor may use any part of a great
variety of books covering many subjects, such as car repair.
Instructions for mostofthe "weekend mechanic" books are close
stylisticaUy to expert system rules. Contents Chapter 1 gives an
introduction to the subject matter; it briefly presents basic
concepts, history, and some perspectives ofexpert systems. Then
itpresents the architecture of an expert system and explains the
stages of building an expert system. The concept of uncertainty in
expert systems and the necessity of deal ing with the phenomenon
are then presented. The chapter ends with the descrip tion of
taxonomy ofexpert systems. Chapter 2 focuses on knowledge
representation. Four basic ways to repre sent knowledge in expert
systems are presented: first-order logic, production sys tems,
semantic nets, and frames. Chapter 3 contains material about
knowledge acquisition. Among machine learning techniques, a
methodofrule learning from examples is explained in de tail. Then
problems ofrule-base verification are discussed. In particular,
both consistency and completeness oftherule base are presented."
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Rough Sets and Knowledge Technology - 7th International Conference, RSKT 2012, Chengdu, China, August 17-20, 2012, Proceedings (Paperback, 2012 ed.)
Tianrui Li, Hung Son Nguyen, Guoyin Wang, Jerzy W.Grzymala- Busse, Ryszard Janicki, …
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R1,636
Discovery Miles 16 360
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 7th
International Conference on Rough Sets and Knowledge Technology,
RSKT 2012, held in Chengdu, China during August 2012, as one of the
co-located conferences of the 2012 Joint Rough Set Symposium, JRS
2012. The 63 revised papers (including 42 regular and 21 short
papers) were carefully reviewed and selected from numerous
submissions. The papers are organized in topical sections on rough
sets and its generalizations, rough sets in data and knowledge
processing, knowledge technology, advances in granular computing
(AGC 2012 workshop), decision-theoretic rough set model and
applications (special session), intelligent decision making and
granular computing (special session), rough set foundations
(special session).
The articles in this volume were selected for presentation at the
Sixth Inter- tional Conference on Rough Sets and Current Trends in
Computing (RSCTC 2008), which took place on October 23-25 in Akron,
Ohio, USA. The conference is a premier event for researchersand
industrial professionals interested in the theory and applications
of rough sets and related methodo- gies. Since its introduction
over 25 years ago by Zdzislaw Pawlak, the theory of rough sets has
grown internationally and matured, leading to novel applications
and theoretical works in areas such as data mining and knowledge
discovery, machine learning, neural nets, granular and soft
computing, Web intelligence, pattern recognition and control. The
proceedings of the conferences in this - ries, as well as in Rough
Sets and Knowledge Technology (RSKT), and the Rough Sets, Fuzzy
Sets, Data Mining and Granular Computing (RSFDGrC) series report a
variety of innovative applications of rough set theory and of its
extensions. Since its inception, the mathematical rough set theory
was closely connected to application ?elds of computer science and
to other areas, such as medicine, which provided additional
motivation for its further development and tested its real-life
value. Consequently, rough set conferences emphasize the -
teractionsandinterconnectionswith relatedresearchareas,
providingforumsfor exchanging ideas and mutual learning. The latter
aspect is particularly imp- tant since the development of rough
set-related applications usually requires a combination of often
diverse expertise in rough sets and an application ?eld
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Rough Sets and Knowledge Technology - Third International Conference, RSKT 2008, Chengdu, China, May 17-19, 2008, Proceedings (Paperback, 2008 ed.)
Guoyin Wang, Tianrui Li, Jerzy W.Grzymala- Busse, Duoqian Miao, Yiyu Y. Yao
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R3,114
Discovery Miles 31 140
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Ships in 10 - 15 working days
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This volume contains the papers selected for presentation at the
Third Inter- tional Conference on Rough Sets and Knowledge
Technology (RSKT 2008) held in Chengdu, P. R. China, May 16-19,
2008. The RSKT conferences were initiated in 2006 in Chongqing, P.
R. China. RSKT 2007 was held in Toronto, Canada, together with
RSFDGrC 2007, as JRS 2007. The RSKT conferences aim to present
state-of-the-art scienti?c - sults, encourage academic and
industrial interaction, and promote collaborative research in rough
sets and knowledge technology worldwide. They place emphasis on
exploring synergies between rough sets and knowledge discovery,
knowledge management, data mining, granular and soft computing as
well as emerging application areas such as bioinformatics,
cognitive informatics, and Web intel- gence, both at the level of
theoretical foundations and real-life applications. RSKT 2008
focused on ?ve major research ?elds: computing theory and
paradigms, knowledge technology, intelligent information
processing, intelligent control, and applications. This was
achieved by including in the conference program sessions on rough
and soft computing, rough mereology with app- cations,
dominance-based rough set approach, fuzzy-rough hybridization, gr-
ular computing, logical and mathematical foundations, formal
concept analysis, data mining, machine learning, intelligent
information processing, bioinform- ics and cognitive informatics,
Web intelligence, pattern recognition, and real-life applications
of knowledge technology. A very strict quality control policy was
adopted in the paper review process of RSKT 2008. Firstly, the PC
Chairs - viewed all submissions.
We would like to present, with great pleasure, the ?rst volume of a
new jo- nal, Transactions on Rough Sets. This journal, part of the
new journal subline in the Springer-Verlag series Lecture Notes in
Computer Science, is devoted to the entire spectrum of rough set
related issues, starting from logical and ma- ematical foundations
of rough sets, through all aspects of rough set theory and its
applications, data mining, knowledge discovery and intelligent
information processing, to relations between rough sets and other
approaches to uncertainty, vagueness, and incompleteness, such as
fuzzy sets, theory of evidence, etc. The ?rst, pioneering papers on
rough sets, written by the originator of the idea, ProfessorZdzis
lawPawlak, werepublishedintheearly1980s.Weareproud to dedicate this
volume to our mentor, Professor Zdzis law Pawlak, who kindly
enriched this volume with his contribution on philosophical,
logical, and mat- matical foundations of roughset theory. In his
paper Professor Pawlakshows all over again the underlying ideas of
rough set theory as well as its relations with Bayes' theorem,
con?ict analysis, ?ow graphs, decision networks, and decision
rules.
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Rough Sets and Current Trends in Computing - 4th International Conference, RSCTC 2004, Uppsala, Sweden, June 1-5, 2004, Proceedings (Paperback, 2004 ed.)
Shusaku Tsumoto, Roman Slowinski, Jan Komorowski, Jerzy W.Grzymala- Busse
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R3,142
Discovery Miles 31 420
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Ships in 10 - 15 working days
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In recent years rough set theory has attracted the attention of
many researchers and practitioners all over the world, who have
contributed essentially to its development and applications.
Weareobservingagrowingresearchinterestinthefoundationsofroughsets,
including the various logical, mathematical and philosophical
aspects of rough sets. Some relationships have already been
established between rough sets and other approaches, and also with
a wide range of hybrid systems. As a result, rough sets are linked
with decision system modeling and analysis of complex systems,
fuzzy sets, neural networks, evolutionary computing, data mining
and knowledge discovery, pattern recognition, machine learning, and
approximate reasoning. In particular, rough sets are used in
probabilistic reasoning, granular computing (including information
granule calculi based on rough mereology), intelligent control,
intelligent agent modeling, identi?cation of autonomous s- tems,
and process speci?cation. Methods based on rough set theory alone
or in combination with other - proacheshavebeendiscoveredwith awide
rangeofapplicationsinsuchareasas: acoustics, bioinformatics,
business and ?nance, chemistry, computer engineering (e.g., data
compression, digital image processing, digital signal processing,
p- allel and distributed computer systems, sensor fusion, fractal
engineering), de- sion analysis and systems, economics, electrical
engineering (e.g., control, signal analysis, power systems),
environmental studies, informatics, medicine, mole- lar biology,
musicology, neurology, robotics, social science, software
engineering, spatial visualization, Web engineering, and Web
mining.
3. Textbook for a course in expert systems, if an emphasis is
placed on Chapters 1 to 3 and on a selection of material from
Chapters 4 to 7. There is also the option of using an additional
commercially available sheU for a programming project. In assigning
a programming project, the instructor may use any part of a great
variety of books covering many subjects, such as car repair.
Instructions for mostofthe "weekend mechanic" books are close
stylisticaUy to expert system rules. Contents Chapter 1 gives an
introduction to the subject matter; it briefly presents basic
concepts, history, and some perspectives ofexpert systems. Then
itpresents the architecture of an expert system and explains the
stages of building an expert system. The concept of uncertainty in
expert systems and the necessity of deal ing with the phenomenon
are then presented. The chapter ends with the descrip tion of
taxonomy ofexpert systems. Chapter 2 focuses on knowledge
representation. Four basic ways to repre sent knowledge in expert
systems are presented: first-order logic, production sys tems,
semantic nets, and frames. Chapter 3 contains material about
knowledge acquisition. Among machine learning techniques, a
methodofrule learning from examples is explained in de tail. Then
problems ofrule-base verification are discussed. In particular,
both consistency and completeness oftherule base are presented."
|
Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - 15th International Conference, RSFDGrC 2015, Tianjin, China, November 20-23, 2015, Proceedings (Paperback, 1st ed. 2015)
Yiyu Yao, Qinghua Hu, Hong Yu, Jerzy W.Grzymala- Busse
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R3,112
Discovery Miles 31 120
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Ships in 10 - 15 working days
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This book constitutes the refereed conference proceedings of the
15th International Conference on Rough Sets, Fuzzy Sets, Data
Mining and Granular Computing, RSFDGrC 2015, held in Tianjin, China
in November 2015 as one of the co-located conference of the 2015
Joint Rough Set Symposium, JRS 2015. The 44 papers were carefully
reviewed and selected from 97 submissions. The papers in this
volume cover topics such as rough sets: the experts speak;
generalized rough sets; rough sets and graphs; rough and fuzzy
hybridization; granular computing; data mining and machine
learning; three-way decisions; IJCRS 2015 data challenge.
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