Linguistic recognition system based on approximate reasoning

Pal, Sankar K.; Mandal, Deba Prasad

doi:10.1016/0020-0255(92)90037-9

Cited by 62 publications

(18 citation statements)

References 13 publications

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“…Four statistical methods (linear and quadratic discriminant analysis, two layer perceptron-with a hidden layer of size equal to half the number of features-and nearest neighbor) plus four well-known fuzzy descriptive rulebased classifiers (Wang and Mendel's [27], Ishibuchi's [15], Pal and Mandal's [22] and genetic iterative learning [5]) were compared to fuzzy Adaboost.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…2. The fitness function derived from confidence-rated Adaboost is defined by (22) The formulas for determining the number of votes and for weight updates in the same approach also need to be adapted. The number of votes is the value that minimizes (23) and the weights are updated according (24) The analytical approximation in (17) and (18) can be used without modifications.…”

Section: F Extension To Multiclass Problemsmentioning

confidence: 99%

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Induction of Fuzzy-Rule-Based Classifiers With Evolutionary Boosting Algorithms

Jesus

Hoffmann

Navascues

et al. 2004

IEEE Trans. Fuzzy Syst.

127

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Abstract-This paper proposes a novel Adaboost algorithm to learn fuzzy-rule-based classifiers. Connections between iterative learning and boosting are analyzed in terms of their respective structures and the manner these algorithms address the cooperation-competition problem. The results are used to explain some properties of the former method. The evolutionary boosting scheme is applied to approximate and descriptive fuzzy-rule bases. The advantages of boosting fuzzy rules are assessed by performance comparisons between the proposed method and other classification schemes applied on a set of benchmark classification tasks.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: F Extension To Multiclass Problemsmentioning

confidence: 99%

Induction of Fuzzy-Rule-Based Classifiers With Evolutionary Boosting Algorithms

Jesus

Hoffmann

Navascues

et al. 2004

IEEE Trans. Fuzzy Syst.

127

View full text Add to dashboard Cite

show abstract

“…Following Pal's idea of a general recognizer [2], the model is divided in two main parts, one for learning and another for processing, as shown in figure 1. …”

Section: The Fuzzy Neural Network Modelmentioning

confidence: 99%

“…The general system architecture was inspired by the work presented by Pal in [1,2]. The idea of using a relational neural network was taken from the general classifier presented by Pedrycz in [3].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Relational Neural Network for Pattern Classification

Suaste-Rivas

Reyes-Galaviz

Díaz-Méndez

et al. 2004

Lecture Notes in Computer Science

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Abstract. In this paper we describe the implementation of a fuzzy relational neural network model. In the model, the input features are represented by fuzzy membership, the weights are described in terms of fuzzy relations. The output values are obtained with the max-min composition, and are given in terms of fuzzy class membership values. The learning algorithm is a modified version of back-propagation. The system is tested on an infant cry classification problem, in which the objective is to identify pathologies in recently born babies.

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“…4 These sys tems are capable of handling various imprecise inputs and in providing multiple class choices corresponding to any input. The present article describes an applica tion the recognition system 3 in detecting roadlike structures from Indian "_emote Sensing satellite (IRS) imagery.…”

Section: Introductionmentioning

confidence: 99%

A Remote Sensing Application of a Fuzzy Classifier

Mandal

Murthy

Pal

1994

Int. J. Unc. Fuzz. Knowl. Based Syst.

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A multivalued (fuzzy) recognition system was formulated by Mandel d. al. which is capable of handling various imprecise inputs and provides multiple class choices to any input. In the present article, an application of the recognition system for detecting road like structures from lndian Remote Sensing satellite (IRS) imagery has been described. The concept of multiple choices of the recognition system is found to be quite useful here. The results are seen to agree well with the geographical maps when various image frames were considered as input.J( eywords: Remote Sensing; Fuzzy Sets; Management of Uncertainty.

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Linguistic recognition system based on approximate reasoning

Cited by 62 publications

References 13 publications

Induction of Fuzzy-Rule-Based Classifiers With Evolutionary Boosting Algorithms

Induction of Fuzzy-Rule-Based Classifiers With Evolutionary Boosting Algorithms

A Fuzzy Relational Neural Network for Pattern Classification

A Remote Sensing Application of a Fuzzy Classifier

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