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How Good Are Fuzzy IfThen Classifiers?
"...  This paper gives some known theoretical results about fuzzy rulebased classiers and oers a few new ones. The ability of TakagiSugenoKang (TSK) fuzzy classiers to match exactly and to approximate classi cation boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a c ..."
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Cited by 14 (0 self)
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 This paper gives some known theoretical results about fuzzy rulebased classiers and oers a few new ones. The ability of TakagiSugenoKang (TSK) fuzzy classiers to match exactly and to approximate classi cation boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a
Selecting fuzzy ifthen rules for classification problems using genetic algorithms
 IEEE TRANS. FUZZY SYST
, 1995
"... This paper proposes a geneticalgorithmbased method for selecting a small number of significant fuzzy ifthen rules to construct a compact fuzzy classification system with high classification power. The rule selection problem is formulated as a combinatorial optimization problem with two objectives ..."
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Cited by 132 (21 self)
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objectives: to maximize the number of correctly classified patterns and to minimize the number of fuzzy ifthen rules. Genetic algorithms are applied to this problem. A set of fuzzy ifthen rules is coded into a string and treated as an individual in genetic algorithms. The fitness of each individual
Fuzzy ifthen rulebased nonlinear classifier
 INT. J. APPL. MATH. COMPUT. SCI
, 2003
"... This paper introduces a new classifier design method that is based on a modification of the classical HoKashyap procedure. The proposed method uses the absolute error, rather than the squared error, to design a linear classifier. Additionally, easy control of the generalization ability and robustne ..."
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Cited by 1 (1 self)
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and robustness to outliers are obtained. Next, an extension to a nonlinear classifier by the mixtureofexperts technique is presented. Each expert is represented by a fuzzy ifthen rule in the TakagiSugenoKang form. Finally, examples are given to demonstrate the validity of the introduced method.
A FUZZY SYSTEM WITH �INSENSITIVE LEARNING OF PREMISES AND CONSEQUENCES OF IF–THEN RULES
"... First, a fuzzy system based on ifthen rules and with parametric consequences is recalled. Then, it is shown that the global and local �insensitive learning of the above fuzzy system may be presented as a combination of both an �insensitive gradient method and solving a system of linear inequaliti ..."
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First, a fuzzy system based on ifthen rules and with parametric consequences is recalled. Then, it is shown that the global and local �insensitive learning of the above fuzzy system may be presented as a combination of both an �insensitive gradient method and solving a system of linear
Optimization under fuzzy ifthen rules
 FUZZY SETS AND SYSTEMS
, 2001
"... The aim of this paper is to introduce a novel statement of fuzzy mathematical programming problems and to provide a method for finding a fair solution to these problems. Suppose we are given a mathematical programming problem in which the functional relationship between the decision variables and th ..."
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and the objective function is not completely known. Our knowledgebase consists of a block of fuzzy ifthen rules, where the antecedent part of the rules contains some linguistic values of the decision variables, and the consequence part consists of a linguistic value of the objective function. We suggest the use
Investigating the Logical Structure of FUZZY IFTHEN. . .
 Accepted Paper, 28th World Automation Congress 1998 (WAC '98
, 1998
"... In developing a semantics for a Fuzzy IfThen Rule Base we in principle distinguish the following two approaches. Firstly, a Fuzzy IfThen Rule Base is considered as a Fuzzy Knowledge Base describing (time independent) situations by means of fuzzy logic. Secondly, a Fuzzy IfThen Rule Base describes ..."
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Cited by 4 (2 self)
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In developing a semantics for a Fuzzy IfThen Rule Base we in principle distinguish the following two approaches. Firstly, a Fuzzy IfThen Rule Base is considered as a Fuzzy Knowledge Base describing (time independent) situations by means of fuzzy logic. Secondly, a Fuzzy IfThen Rule Base
Fuzzy IfThen Rules Extraction by Means of εInsensitive Learning Techniques Integrated with Deterministic Annealing Optimization Method
"... Abstract — This paper introduces the research on possibility of global optimization elements and εinsensitive learning techniques integration in aim of fuzzy ifthen rules extraction quality increase. The new learning algorithm of neurofuzzy system with parameterized consequents is introduced. It ..."
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Abstract — This paper introduces the research on possibility of global optimization elements and εinsensitive learning techniques integration in aim of fuzzy ifthen rules extraction quality increase. The new learning algorithm of neurofuzzy system with parameterized consequents is introduced
FUZZY IFTHEN RULE INDUCTION WITH CUMULATIVE INFORMATION ESTIMATIONS APPLIED TO REALWORLD DATA
"... Realworld data containing instances corresponding to patients with otoneurological diseases were explored with fuzzy IFTHEN rule induction. It was based on transformation of a fuzzy decision tree made with using cumulative information estimations as the locally optimal criterion at its nodes. This ..."
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Realworld data containing instances corresponding to patients with otoneurological diseases were explored with fuzzy IFTHEN rule induction. It was based on transformation of a fuzzy decision tree made with using cumulative information estimations as the locally optimal criterion at its nodes
Fuzzy extractors: How to generate strong keys from biometrics and other noisy data. Technical Report 2003/235, Cryptology ePrint archive, http://eprint.iacr.org, 2006. Previous version appeared at EUROCRYPT 2004
 34 [DRS07] [DS05] [EHMS00] [FJ01] Yevgeniy Dodis, Leonid Reyzin, and Adam
, 2004
"... We provide formal definitions and efficient secure techniques for • turning noisy information into keys usable for any cryptographic application, and, in particular, • reliably and securely authenticating biometric data. Our techniques apply not just to biometric information, but to any keying mater ..."
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Cited by 532 (38 self)
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material that, unlike traditional cryptographic keys, is (1) not reproducible precisely and (2) not distributed uniformly. We propose two primitives: a fuzzy extractor reliably extracts nearly uniform randomness R from its input; the extraction is errortolerant in the sense that R will be the same even
Results 1  10
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