Displaying similar documents to “A practical application of kernel-based fuzzy discriminant analysis”

Spectral fuzzy classification system: a supervised approach.

Ana Del Amo, Daniel Gómez, Javier Montero (2003)

Mathware and Soft Computing

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The goal of this paper is to present all algorithm for pattern recognition, leveraging on an existing fuzzy clustering algorithm developed by Del Amo et al. [3, 5], and modifying it to its supervised version, in order to apply the algorithm to different pattern recognition applications in Remote Sensing. The main goal is to recognize the object and stop the search depending on the precision of the application. The referred algorithm was the core of a classification system based on Fuzzy...

A neuro-fuzzy system for isolated hand-written digit recognition.

Miguel Pinzolas, José Javier Astrain, Jesús Villadangos, José Ramón González de Mendívil (2001)

Mathware and Soft Computing

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A neuro-fuzzy system for isolated hand-written digit recognition using a similarity fuzzy measure is presented. The system is composed of two main blocks: a first block that normalizes the input and compares it with a set of fuzzy patterns, and a second block with a multilayer perceptron to perform a neuronal classification. The comparison with the fuzzy patterns is performed via a fuzzy similarity measure that uses the Yager parametric t-norms and t-conorms. Along this work, several...

A fuzzy if-then rule-based nonlinear classifier

Jacek Łęski (2003)

International Journal of Applied Mathematics and Computer Science

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This paper introduces a new classifier design method that is based on a modification of the classical Ho-Kashyap 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 robustness to outliers are obtained. Next, an extension to a nonlinear classifier by the mixture-of-experts technique is presented. Each expert is represented by a fuzzy if-then rule in the Takagi-Sugeno-Kang...

Rule-based fuzzy object similarity.

Horst Bunke, Xavier Fábregas, Abraham Kandel (2001)

Mathware and Soft Computing

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A new similarity measure for objects that are represented by feature vectors of fixed dimension is introduced. It can simultaneously deal with numeric and symbolic features. Also, it can tolerate missing feature values. The similarity measure between two objects is described in terms of the similarity of their features. IF-THEN rules are being used to model the individual contribution of each feature to the global similarity measure between a pair of objects. The proposed similarity...

Evolution-fuzzy rule based system with parameterized consequences

Piotr Czekalski (2006)

International Journal of Applied Mathematics and Computer Science

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While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm. The presented method consists of the following steps:...

Extraction of fuzzy rules using deterministic annealing integrated with ε-insensitive learning

Robert Czabański (2006)

International Journal of Applied Mathematics and Computer Science

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A new method of parameter estimation for an artificial neural network inference system based on a logical interpretation of fuzzy if-then rules (ANBLIR) is presented. The novelty of the learning algorithm consists in the application of a deterministic annealing method integrated with ε-insensitive learning. In order to decrease the computational burden of the learning procedure, a deterministic annealing method with a "freezing" phase and ε-insensitive learning by solving a system of...

A fuzzy system with ε-insensitive learning of premises and consequences of if-then rules

Jacek Łęski, Tomasz Czogała (2005)

International Journal of Applied Mathematics and Computer Science

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First, a fuzzy system based on ifFirst, a fuzzy system based on if-then rules and with parametric consequences is recalled. Then, it is shown that the globalthen rules and with parametric consequences is recalled. Then, it is shown that the global and local ε-insensitive learning of the above fuzzy system may be presented as a combination of both an ε-insensitive gradient method and solving a system of linear inequalities. Examples are given of using the introduced method to design fuzzy...

Application of fuzzy techniques to the design of algorithms in computer vision.

Eduard Montseny, Pilar Sobrevilla (1998)

Mathware and Soft Computing

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In this paper a method for the design of algorithms is presented which use fuzzy techniques in order to achieve a better vagueness treatment. A base of rules will be developed in order to design the algorithms. Data fuzzification problem is solved by using probability density functions and probability distribution functions, whereas data analysis is set out associating, to each one of the analysis rules, a fuzzy set which will be obtained by applying an aggregation function which will...