Kernel-based linear-threshold algorithms, such as support vector machines and Perceptron-like algorithms, are among the best available techniques for solving pattern classification problems. In this paper, we describe an extension of the classical Perceptron algorithm, called second-order Perceptron, and analyze its performance within the mistake bound model of on-line learning. The bound achie...

This paper presents a novel learning algorithm of fuzzy perceptron neural networks (FPNNs) for classifiers that utilize expert knowledge represented by fuzzy IF-THEN rules as well as numerical data as inputs. The conventional linear perceptron network is extended to a second-order one, which is much more flexible for defining a discriminant function. In order to handle fuzzy numbers in neural n...

The perceptron (also referred to as McCulloch-Pitts neuron, or linear threshold gate) is commonly used as a simplified model for the discrimination and learning capability of a biological neuron. Criteria that tell us when a perceptron can implement (or learn to implement) all possible dichotomies over a given set of input patterns are well known, but only for the idealized case, where one assu...

The perceptron algorithm is a simple iterative procedure for finding a point in a convex cone F . At each iteration, the algorithm only involves a query to a separation oracle for F and a simple update on a trial solution. The perceptron algorithm is guaranteed to find a point in F after O(1/τ F ) iterations, where τF is the width of the cone F . We propose a version of the perceptron algorithm...

Digital straight lines can be charactarized by Rosenfeld's chord property as well as Hung's evenness property. Both properties can be formulated in terms of linear inequalities. Because linear inequalities can be veriied by the predicates of a Gamba perceptron, this leads in a straightforward manner to the design of perceptrons that can recognize digital straight lines. In this paper we will di...

A fast method for sizing the multilayer perceptron is proposed. The principal assumption is that a modular network with the same theoretical pattern storage as the multilayer perceptron has the same training error. This assumption is analyzed for the case of random patterns. Using several benchmark datasets, the validity of the approach is demonstrated.

In this paper, we will consider the problem of classifying electroencephalogram (EEG) signals of normal subjects, and subjects suffering from psychiatric disorder, e.g., obsessive compulsive disorder, schizophrenia, using a class of artificial neural networks, viz., multi-layer perceptron. It is shown that the multilayer perceptron is capable of classifying unseen test EEG signals to a high deg...

Kernel Perceptron is very simple and efficient online classification algorithm. However, it requires increasingly large computational resources with data stream size and is not applicable on large-scale problems or on resource-limited computational devices. In this paper we describe implementation of Kernel Perceptron on ATTiny2313 microcontroller, one of the most primitive computational device...

The subject of this thesis is the original study of the application of the multi-layer perceptron architecture to channel equalization in digital communications systems. Both theoretical analyses and simulations were performed to explore the performance of the perceptron-based equalizer (including the decision feedback equalizer). Topics covered include the factors that affect performance of th...