We propose a method of designing a reduced complexity nearest-neighbor classifier with near-minimal computational complexity from a given nearest-neighbor classifier that has high input dimensionality and a large number of class vectors. We applied our method to the classification problem of handwritten numerals in the NIST database. If the complexity of the RCNN classifier is normalized to that of the given classifier, the complexity of the derived classifier is 62 percent, 2 percent higher than that of the optimal classifier. This was found using the exhaustive search.

A very high precision is needed to implement the adder using stochastic computation (or pulse arithmetic) in modern VLSI technology. In this paper we propose a new model of perceptron using random bitstreams that alleviates this problem.

For many real applications of pattern classification problems, it is more important to reduce the misclassification rate than to increase the rate of successful classification. In this paper, we propose a single-layer neural network with two rejection mechanisms for character recognition problems, which guarantees a very low misclassification rate. The proposed architecture is a cascaded connection of an SLP network and a simple combinational circuit. Comparing to the MLP network, it yields fast learning and requires a simple hardware architecture. We also introduce a new linearly separable coding scheme for training the SLP network to reduce the misclassification rate. We prepared two databases: one with 135,000 digit patterns and the other with 117,000 letter patterns. Then we applied the proposed method to the classification problem for the databases and results show that the misclassification rate is significantly low with maintaining a high recognition rate.>

Stochastic computation uses pulse streams to represent numbers. In this paper, we have studied the novel method to implement the number system which uses the ratio of the number of one (high) pulses and the number of zero (low) pulses in a pulse stream. With this number system, if we let P be the probability that the pulse is one in a pulse stream, then the number Y we want to deal with is defined as Y=P/(1-P). We have studied the method to implement the basic operations such as an addition, a multiplication and a division using this number system and considered the error characteristics of such operations in stochastic computation. Also we introduce an averaging circuit to reduce the error which is inherent in stochastic computation. According to the study, by using this number system we can implement artificial neural networks and represent the simulation results for the digit recognition problem.>