Applied Soft Computing Technologies: The Challenge of by Ajith Abraham

By Ajith Abraham

This quantity offers the court cases of the ninth on-line global convention on tender Computing in business functions (WSC9), September twentieth - October 08th, 2004, hung on the area huge internet. It includes plenary lectures, unique papers and tutorials offered in the course of the convention. The e-book brings jointly notable learn and advancements within the box of soppy computing (evolutionary computation, fuzzy good judgment, neural networks, and their fusion) and its functions in technological know-how and expertise.

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Example: Classification of University Students This section provides an example to illustrate the steps involved in constructing decision trees.

Fuentes The Bayes Theorem is a statistical concept that can be used as a basis for data mining techniques, such as pattern classification and discrimination. It can also be used as a starting point for implementing more complex data mining and Knowledge Discovery Database (KDD) techniques such as Bayesian networks [122]. The Bayes approach can also be used in combination with other methodologies such as Markov Chains and other probabilistic techniques, many of which are used to build innovative models such as the Relational Bayesian Classifiers for relational data sets [112].

With the training data available, the Bayes methodology is used to train a classifier. , 1 n (xi - n){xi - fj,) , " i=\ where {xi}f=1 are each of the samples in the appropriate class. /2 -(l/2)(x-p,)^i(x-fl) With a probability density function for each class, it is possible to compute the posterior probabilities (see Sec. 2) using the Bayes Theorem. For example, the probability that x belongs in class j can be determined [40] by P{mlx) = P{x\Wj)P{Wj) P3(x) • The classifier is then built using these posterior probabilities and once it has been established, it can be used to classify and validate the data set.

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