Using rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to let the learning algorithm influence the selection of training examples. The presented method estimates the utility of new experiments based on the knowledge represented by the existing rulebase. An extended rule format allows to deal with uncertainty. Exper...

This paper presents an automatic learning algorithm which generates a fuzzy rule based knowledge representation. While learning the membership functions and rules the internal structure of the rule base is also considered. This is done by definition of 1 a complexity cost function and 2 a minimal Fuzzy System. A Genetic Algorithm is used to estimate the Fuzzy Systems which capture a low comlex ...

We introduce a learning algorithm for multilayer neural networks composed of binary linear threshold elements. Whereas existing algorithms reduce the learning process to minimizing a cost function over the weights, our method treats the internal representations as the fundamental entities to be determined. Once a correct set of internal representations is arrived at, the weights are found by th...

Now days, Word Sense Disambiguation (WSD) is a vital area which is very useful in today’s world. Many WSD algorithms are available in literature; we have chosen to an optimal and portable WSD algorithm. We are discussed the supervised, unsupervised, and knowledge-based approaches for WSD. In this paper we are discuses that association rules, Knowledge-based WSD, Corpus-based WSD.

In rule learning, rules are typically induced in two phases, rule refinement and rule selection. It was recently argued that the usage of two separate heuristics for each phase—in particular using the so-called inverted heuristic in the refinement phase—produces longer rules with comparable classification accuracy. In this paper we test the utility of inverted heuristics in the context of subgr...

Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...

The paper describes an inductive learning environment called DELVAUX for classiication tasks that learns PROSPECTOR-style, Bayesian rules from sets of examples. A genetic algorithm approach is used for learning Bayesian rule-sets, in which a population consists of sets of rule-sets that generate oosprings through the exchange of rules, permitting tter rule-sets to produce oosprings with a highe...

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based class...

Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is influenced by an environmental signal, termed a reward, that directs the changes in appropriate directions. We apply a recently introduced policy learning algorithm from machine learning to networks of spiking neurons a...