A novel relational learning approach that tightly integrates the naı̈ve Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naı̈ve Bayes only for post-processing the rule sets, the presented approach employs the naı̈ve Bayes criterion to guide its search directly. The proposed technique is implemented in...

We demonstrate in this article that a Hebb-like learning rule with memory paves the way for active learning in the context of recurrent neural networks. We compare active with passive learning and a Hebb-like learning rule with and without memory for the problem of timing to be learned by the neural network. Moreover, we study the influence of the topology of the recurrent neural network. Our r...

A novel relational learning approach that tightly integrates the naı̈ve Bayes learning scheme with the inductive logic programming rule-learner FOIL is presented. In contrast to previous combinations that have employed naı̈ve Bayes only for post-processing the rule sets, the presented approach employs the naı̈ve Bayes criterion to guide its search directly. The proposed technique is implemented in...

Di fferent learning algorithms based on learning from examples are described based on a set of graph rewrite rules. Starting from either a very general or a very special rule set which is modeled as graph, two to three basic rewrite rules are applied until a rule graph explaining all examples is reached. The rewrite rules can also be used to model the corresponding hypothesis space as they desc...

FuzzyBexa was the first algorithm to use a set covering approach for induction of fuzzy classification rules. It followed an iterated concept learning strategy, where rules are induced for each concept in turn. We present a new algorithm to allow also simultaneous concept learning and the induction of ordered fuzzy rule sets. When a proper rule evaluation function is used, simultaneous concept ...

We present a novel stochastic Hebb-like learning rule for neural networks. This learning rule is stochastic with respect to the selection of the time points when a synaptic modification is induced by preand postsynaptic activation. Moreover, the learning rule does not only affect the synapse between preand postsynaptic neuron which is called homosynaptic plasticity but also on further remote sy...

Association rule learning is a data mining task that tries to discover interesting relations between variables in large databases. A review of association rule learning is presented that focuses on the use of evolutionary algorithms not only applied to Boolean variables but also to categorical and quantitative ones. The use of fuzzy rules in the evolutionary algorithms for association rule lear...