Papers by Juan Luis Castro
Fuzzy measures, integrals and quantification in artificial intelligence problems - An homage to Miguel Delgado
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics
Interpretation of Support Vector Machines by means of Fuzzy Rule-Based Systems
European Society for Fuzzy Logic and Technology, 2005
Support Vector Machines (SVM) have demon- strated their ability in solving classiflcation prob- l... more Support Vector Machines (SVM) have demon- strated their ability in solving classiflcation prob- lems in an optimal way with a solid mathematical background. In this paper we improve the inter- pretability of SVM's by showing that every SVM is exactly represented by a Fuzzy Rule Based- System, for every kernel function used. Neverthe- less, this system is in some way
The relationship between support vector machines (SVMs) and Takagi-Sugeno-Kang (TSK) fuzzy system... more The relationship between support vector machines (SVMs) and Takagi-Sugeno-Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for every used kernel function. Restricted methods to extract rules from SVMs have been previously published. Their limitations are surpassed with the presented extraction method. The behavior of SVMs is explained by means of fuzzy
Smooth transition autoregressive models and fuzzy rule-based systems: Functional equivalence and consequences
Fuzzy Sets and Systems, Dec 1, 2007
In this work we will explore the theoretical connections existing between fuzzy rule-based system... more In this work we will explore the theoretical connections existing between fuzzy rule-based systems (FRBS) applied on univariate time series and two statistical reference tools, the autoregressive (AR) models and the smooth transition autoregressive (STAR) model. We ...
Fuzzy logics as families of bivaluated logics
Fuzzy Sets and Systems, Jun 24, 1994
ABSTRACT
The aim of this paper is to present a method for identifying the structure of a rule in a fuzzy m... more The aim of this paper is to present a method for identifying the structure of a rule in a fuzzy model. For this purpose, an ATMS shall be used. An algorithm obtaining the identification of the structure will be suggested. The minimal structure of the rule (with respect to the number of variables that must appear in the rule) will be found by this algorithm. Furthermore, the identification parameters shall be obtained simultaneously. The proposed method shall be applied for classification in an example. The Iris Plant Database shall be learnt for all three kinds of plants. @ 1997 Elsevier Science B.V.
Non-Monotonic Reasoning in Multivalued and Fuzzy Logic
The aim of this paper is to provide a tool which makes possible non-monotonicreasoning in a propo... more The aim of this paper is to provide a tool which makes possible non-monotonicreasoning in a propositional knowledge system based on multivalued logic with certaintyfactors and fuzzy logic. The support system which results in the non-monotony willbe a Truth Maintenance System (TMS). Particularly, we will use ATMS (TMS basedon assumptions) defined by De Kleer. From this ATMS we will extend its use incase we have monotonic reasoning systems based on [0,1] valued logic and fuzzy logic.The...
A Natural Language Virtual Tutoring System
Iadis, 2008
Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, Aug 18, 1995
The aim of this paper is to present a method for identifying the structure of a rule in a fuzzy m... more The aim of this paper is to present a method for identifying the structure of a rule in a fuzzy model. For this purpose, an ATMS shall be used (Zurita 1994). An a. lgorithm obtaining the identification of the structure will be suggested (Castro 1995). The minimal structure of the rule (with re spect to the number of variables that must appear in the rule) will be found by this al gorithm. Furthermore, the identification pa rameters shall be obtained simultaneously. The proposed method shall be applied for classification to an example. The Iris Plant Database shall be learnt for all three kinds of plants.
systems: Functional equivalence and consequences
A link-pruning algorithm for neural networks
Eusflat, 1999
Conjuction and disjunction on ([0,1], ≤)
Fuzzy Sets and Systems, 1995
A link-pruning algorithm for neural networks
An Ant Colony Optimization plug-in to Enhance the Interpretability of Fuzzy Rule Bases with Exceptions
Advances in Soft Computing, 2007
Usually, fuzzy rules contain in the antecedent propositions that restrict a variable to a fuzzy v... more Usually, fuzzy rules contain in the antecedent propositions that restrict a variable to a fuzzy value by means of an equal-to predicate. We propose to improve the interpretability of fuzzy models by extending the syntax of their rules. With this aim, on one hand, new predicates are considered in the rule antecedents and, on the other hand, rules can be
Using Ant Colony Optimization for Learning Maximal Structure Fuzzy Rules
The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05., 2005
Page 1. Using Ant Colony Optimization for Learning Maximal Structure Fuzzy Rules Pablo Carmona De... more Page 1. Using Ant Colony Optimization for Learning Maximal Structure Fuzzy Rules Pablo Carmona Department of Computer Science Industrial Engineering School University of Extremadura Badajoz E-06071, Spain E-mail: pablo@unex.es ...
Fuzzy Pairwise Multiclass Support Vector Machines
Lecture Notes in Computer Science, 2006
At first, support vector machines (SVMs) were applied to solve binary classification problems. Th... more At first, support vector machines (SVMs) were applied to solve binary classification problems. They can also be extended to solve multicategory problems by the combination of binary SVM classifiers. In this paper, we propose a new fuzzy model that includes the advantages of several previously published methods solving their drawbacks. For each datum, a class is rejected using information provided
A neuro-fuzzy approach for feature selection
Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2001
A method for feature selection based on a combination of artificial neural network and fuzzy tech... more A method for feature selection based on a combination of artificial neural network and fuzzy techniques is presented. The procedure produces a ranking of features according to their relevance to the network. This ranking is used to perform a backward selection by successively removing input nodes in a network trained using the complete set of features as inputs. Irrelevant input
Combining Both a Fuzzy Inductive Learning and a Fuzzy Repertory Grid Method
Studies in Fuzziness and Soft Computing, 2002
ABSTRACT
Are ANNs Black Boxes
IEEE Transactions on Neural Networks, 1997
Uploads
Papers by Juan Luis Castro