In this paper a classification system, which corisists of a neural network and a decision element, is presented, both parts processing information in series. For the neural network, we propose a training algorithm based on the direct equalization of weights and components of prototype vectors, and a neuronal function that detects similarities between its inputs and the weights. This systematics allows, in addition to a good performance in recognition, an easy, time-controlled reprogramming process of the network, even for large patterns. To test and validate the system, a real classifier is presented and studied, a classifier that is designed to recognize segmented handwritten characters corresponding to the NIST SD19 database and with which good results for digits and lower-case letters are obtained.
Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the results of some well known Machine Learning methods in the resolution of discrete classification problems. A binary version of the PSO algorithm is used to obtain a set of logic rules that map binary masks (that represent the attribute values), to the available classes. This algorithm has been tested both in a single pass mode and in an iterated mode on a well-known set of problems, called the MONKS set, to compare the PSO results against the results reported for that domain by the application of some common Machine Learning algorithms.
Artificial Neural Networks are commonly used in pattern classification, function approximation, optimization, pattern matching, machine learning and associative memories. They are currently being an alternative to traditional statistical methods for mining data sets in order to classify data. Artificial Neural Networks are well-established technology for solving prediction and classification problems, using training and testing data to build a model. However, the success of the networks is highly dependent on the performance of the training process and hence the training algorithm. In this paper, we applied the Artificial Bee Colony (ABC) Optimization Algorithm on training feed-forward neural networks to classify different data sets which are widely used in the machine learning community. The performance of the ABC algorithm is investigated on benchmark classification problems from classification area and the results are compared with the other well-known conventional and evolutionary algorithms. The results indicate that ABC algorithm can efficiently be used on training feed-forward neural networks for the purpose of pattern classification.
The paper examines sources of brain activity, contributing to EEG patterns which correspond to motor imagery during training to control brain-computer interface. To identify individual source contribution into electroencephalogram recorded during the training Independent Component Analysis was used. Then those independent components for which the BCI system classification accuracy was at maximum were treated as relevant to performing the motor imagery tasks, since they demonstrated well exposed event related de-synchronization and event related synchronization of the sensorimotor μ-rhythm during imagining of contra- and ipsilateral hand movements. To reveal neurophysiological nature of these components we have solved the inverse EEG problem to locate the sources of brain activity causing these components to appear in EEG. The sources were located in hand representation areas of the primary sensorimotor cortex. Their positions practically coincide with the regions of brain activity during the motor imagination obtained in fMRI study. Individual geometry of brain and its covers provided by anatomical MR images was taken into account when localizing the sources.