finding frequent patterns plays a key role in exploring association patterns, correlation, and many other interesting relationships that are applicable in tdb. several association rule mining algorithms such as apriori, fp-growth, and eclat have been proposed in the literature. fp-growth algorithm construct a tree structure from transaction database and recursively traverse this tree to extract...

We present a novel approach to machine learning, called ABML (argumentation based ML). This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert’s arguments, or reasons, for some of the learning examples. We require that the theory induced from the examples explains the examples in terms of the given reasons. Thus argument...

Geometric independent component analysis (ICA) uses a weight update rule that is very similar to the self-organizing map (SOM) learning rule in the case of a trivial neighborhood function. In this paper we use this fact and present a new geometric ICA algorithm that uses a SOM for learning. The separation qualiry is better in comparison to other geometric algorithms, but the computational cost ...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

Covering algorithms for learning rule sets tend toward learning concise rule sets based on the training data. This bias may not be appropriate in the domain of text classification due to the large number of informative features these domains typically contain. We present a basic covering algorithm, DAIRY, that learns unordered rule sets, and present two extensions that encourage the rule learne...

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rule-based systems, i.e., evolutionary algorithm-based processes to automatically design fuzzy rulebased systems by learning andror tuning the fuzzy rule b...

Neural network ensemble can significantly improve the generalization ability of neural network based systems. In this paper, a novel rule learning algorithm is proposed, where neural network ensemble acts as a front-end process that generates data for the learning of rules. Experimental results show that the proposed algorithm can generate rules with strong generalization ability.

We explore humans' rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a ...

| In previous work ((1, 2, 3]) we explained how to use standard optimization methods such as simulated annealing, gradient descent and genetic algorithms to optimize a parametric function which could be used as a learning rule for neural networks. To use these methods, we had to choose a xed number of parameters and a rigid form for the learning rule. In this article, we propose to use genetic ...

Genetic algorithms and evolution strategies are combined in order to build a multi-stage hybrid evolutionary algorithm for learning constrained approximate Mamdani-type knowledge bases from examples. The genetic algorithm niche concept is used in two of the three stages composing the learning process with the purpose of improving the accuracy of the designed fuzzy rule-based systems. The propos...