We derive an efficient algorithm for topographic mapping of proximity data (TMP), which can be seen as an extension of Kohonen's self-organizing map to arbitrary distance measures. The TMP cost function is derived in a Baysian framework of folded Markov chains for the description of autoencoders. It incorporates the data by a dissimilarity matrix D and the topographic neighborhood by a matrix H...

In the dynamic global economy, the accuracy in forecasting the foreign currency exchange (Forex) rates or at least predicting the trend correctly is of crucial importance for any future investment. The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times. In this paper, we developed and investigated three Artificial Neural Netwo...

In this paper we describe a close-to-sensor low latency visual processing system. We show that by adaptively sampling visual information, low level tracking can be achieved at high temporal frequencies with no increase in bandwidth and using very little memory. By having close-to-sensor processing, image regions can be captured and processed at millisecond sub-frame rates. If spatiotemporal reg...

This project is aimed to build an efficient, scalable, portable, and trainable part-of-speech tagger. Using 98% of Penn Treebank-3 as the training data, it builds a raw tagger, using Bayes’ theorem, a hidden Markov model, and the Viterbi algorithm. After that, a reinforcement machine learning algorithm and contextual transformation rules were applied to increase the tagger’s accuracy. The tagge...

In practical pattern recognition applications, the nearest neighbor classifier (NNC) is often applied because it does not require an a priori knowledge of the joint probability density of the input feature vectors. As the number of example vectors is increased, the error probability of the NNC approaches that of the Baysian classifier. However, at the same time, the computational complexity of ...