In this paper we propose a segmentation system for unconstrained Arabic online handwriting. An essential problem addressed by analytical-based word recognition system. The system is composed of two-stages the first is a newly special designed hidden Markov model (HMM) and the second is a rules based stage. In our system, handwritten words are broken up into characters by simultaneous segmentati...

Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns. However, discovery of such hidden patterns may disclose some sensitive information. As a result privacy becomes one of the prime concerns in data mining research. Since distributed association mining discovers global association rules by combining models from various distributed sites...

Probabilistic inference networks capture the stochastic relation between variables by ‘directed’ probabilistic rules corresponding to conditional probabilities, e.g. p(Ak|Ai∧Aj). Associative neural networks – like Boltzmann machine networks – yield a joint distribution, which is a special case of the distribution generated by inference networks. In this paper conventional associative neural net...

The idea of using RBF neural networks for fuzzy rule extraction from numerical data is not new. The structure of this kind of architectures, which supports clustering of data samples, is favorable for considering clusters as if-then rules. However, in order for real if-then rules to be derived, proper antecedent parts for each cluster need to be constructed by selecting the appropriate subspace...

Increasingly, researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. For instance, Repertory Grids, Formal Concept Analysis (FCA) and Ripple-Down Rules (RDR) all integrate either implicit or explicit contextual information. However, these methodologies treat context as a static entity, neglecting many connectionists’ work in learning hidden an...

This paper presents a rejection strategy for a convolutional neural network. The method is based on Viterbi paths generated by a context based 2D stochastic model for rejected image correction. The rejection strategy is an important issue in neural network theory. The challenge is to find rules, which determine if an image is correctly classified or not. Applying strong rules leads to the rejec...

Radial basis neural (RBF) networks provide an excellent solution to many pattern recognition and classi cation problems. However, RBF networks are also a local representation technique that enables the easy conversion of the hidden units into symbolic rules. This paper examines rules extracted from RBF networks. We use the iris ower classication task and a vibration diagnosis classi cation task...

A new algorithm for neural network pruning is presented. Using this algorithm, networks with small number of connections and high accuracy rates for breast cancer diagnosis are obtained. We will then describe how rules can be extracted from a pruned network by considering only a nite number of hidden unit activation values. The accuracy of the extracted rules is as high as the accuracy of the p...

In a Markov decision problem with hidden state variables, a posterior distribution serves as a state variable and Bayes’ law under the approximating model gives its law of motion. A decision maker expresses fear that his model is misspecified by surrounding it with a set of alternatives that are nearby as measured by their expected log likelihood ratios (entropies). Sets of martingales represen...

Tying of Hidden Markov Model states is an important issue for the use of triphones as modeling units in automatic speech recognition systems. This paper studies the application of a–priori rules for tying in combination with data driven methods. The baseline method features a combination of a–priori rules that reduce the theoretical number of units by an oder of magnitude and a simple back–off ...