Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and other related models. >

Multilayer perceptron, fuzzy sets, and classification

Fuzzy Rule-Based Systems Evolutionary Computation Introduction to Genetic Fuzzy Systems Genetic Tuning Processes Learning with Genetic Algorithms Genetic Fuzzy Rule-Based Systems Based on the Michigan Approach Genetic Fuzzy Rule-Based Systems Based on the Pittsburgh Approach Genetic Fuzzy Rule-Based Systems Based on the lterative Rule Learning Approach Other Genetic Fuzzy Rule-Based System Other Kinds of Evolutionary Fuzzy Systems Applications.

Genetic Fuzzy Systems: Evolutionary Tuning And Learning Of Fuzzy Knowledge Bases

A proposal on reasoning methods in fuzzy rule-based classification systems

This paper presents an efficient fuzzy classifier with the ability of feature selection based on a fuzzy entropy measure. Fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. With this information, we can partition the pattern space into nonoverlapping decision regions for pattern classification. Since the decision regions do not overlap, both the complexity and computational load of the classifier are reduced and thus the training time and classification time are extremely short. Although the decision regions are partitioned into nonoverlapping subspaces, we can achieve good classification performance since the decision regions can be correctly determined via our proposed fuzzy entropy measure. In addition, we also investigate the use of fuzzy entropy to select relevant features. The feature selection procedure not only reduces the dimensionality of a problem but also discards noise-corrupted, redundant and unimportant features. Finally, we apply the proposed classifier to the Iris database and Wisconsin breast cancer database to evaluate the classification performance. Both of the results show that the proposed classifier can work well for the pattern classification application.

An efficient fuzzy classifier with feature selection based on fuzzy entropy

Approximate Reasoning is the process or processes by which a possible imprecise conclusion is deduced from a collection of imprecise premises. Fuzzy logic plays the major role in approximate reasoning. It has the ability to deal with different types of uncertainty.An overview of the different aspects of the theory of approximate reasoning has been provided here based on the existing literature. Suitable illustrations are included, whenever necessary, to make the concept clear. Some of the implementation of the theory to real life problems have been mentioned. Finally, a linguistic recognition system based on approximate reasoning has been described along with its implementation in speech recognition problem.

https://www.isical.ac.in/~sankar/paper/60.pdf

Fuzzy Logic and Approximate Reasoning: An Overview

https://www.isical.ac.in/~sankar/paper/64.pdf

Linguistic recognition system based on approximate reasoning

A recognition system based on fuzzy set theory and approximate reasoning that is capable of handling various imprecise input patterns and providing a natural decision system is described. The input feature is considered to be of either quantitative form, linguistic form, mixed form, or set form. The entire feature space is decomposed into overlapping subdomains depending on the geometric structure and the relative position of the pattern classes found in the training samples. Uncertainty (ambiguity) in the input statement is managed by providing/modifying membership values to a great extent. A relational matrix corresponding to the subdomains and the pattern classes is used to recognize the samples. The system uses L.A. Zadeh's (1977) compositional rule of inference and gives a natural (linguistic) multivalued output decision associated with a confidence factor denoting the degree of certainty of a decision. The effectiveness of the algorithm is demonstrated for some artificially generated patterns and for real-life speech data. >

Formulation of a multivalued recognition system

Determining the polarity and strength of opinions is an important research area over the last few years. The work challenges on opinion sentences and opinion holder extraction, opinion polarity judgment and also to measure the strength of polarity. In this paper we propose a convenient way using fuzzy techniques for analyzing opinion content in a review; our main goal is to analyze and to evaluate the sentiment in the review into a score for decision-making. The web contains product reviews and consumers are often forced to wade through many on-line reviews in order to make a product choice. We use techniques that decompose the review sentences and evaluate the individual characteristics of a product. Our task is performed in three steps: (1) mining product features that have been commented by customers; (2) identifying opinion sentences in each review and extracting the opinion phrases in each opinion sentence; (3) to measure the strength of opinion phrases to summarize the results. This paper introduces FOM (Fuzzy Opinion Miner), a supervised opinion orientation detection system that mines reviews to build a model of important product features, their evaluation by reviewers and the over all importance of the reviews.

http://www.aicit.org/ijei/ppl/05-IJEI1-101013IP.pdf

Deba Prasad Mandal

Papers

Fuzzy Logic and Approximate Reasoning: An Overview

Linguistic recognition system based on approximate reasoning

Formulation of a multivalued recognition system

Finding Opinion Strength Using Fuzzy Logic on Web Reviews

Selection of alpha for alpha-hull in r2