In this paper we e v aluate the performance of Support Vector Machines SVMs and Multi-Layer Perceptrons MLPs on two diierent problems of Particle Identiication in High Energy Physics experiments. The obtained results indicate that SVMs and MLPs tend to perform very similarly.

This paper considers the similarity between two measures of air pollution/quality control, on the one hand, and widely used indicators of life quality and welfare, on the other. We have developed a multi-layer perceptron neural network system which is trained to predict the measurements of air quality (emissions of sulphur and nitrogen oxides), using Eurostat data for 34 countries. We used life...

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will conc...

In the eighties the problem of the lack of an efficient algorithm to train multilayer Rosenblatt perceptrons was solved by sigmoidal neural networks and backpropagation. But should we still try to find an efficient algorithm to train multilayer hardlimit neuronal networks, a task known as a NP-Complete problem? In this work we show that this would not be a waste of time by means of a counter ex...

PURPOSE To evaluate the performance of support vector machine, multi-layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps. METHODS A total of 318 maps were selected and classified into four categories: normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive keratectomy (n = 11). For each map, 11 attr...

223 Abstract— Data mining techniques are broadly classified into two classes (i) Statistical Techniques and (ii) Knowledge Discovery. The continuing rapid growth of on-line data and the widespread use of databases necessitate the development of techniques for extracting useful knowledge and for facilitating database access. This paper analyzes the results of multilayer perceptron with pace regr...

The application of data mining and machine learning in directing clinical research into possible hidden knowledge is becoming greatly influencial in cancer research. This research presents a comparison of three data mining classification models: multi-layer perceptron neural networks, C4.5 decision trees and Naive Bayes. The classification models are built for breast cancer survivability predic...

Statistical mechanics is applied to estimate the maximal capacity per weight (a,) of a two-layer feed-forward network with discrete weights of depth 1, functioning as a parity machine of the K hidden units. For each K and lSZo(K'), the maximal theoretical capacity a, = 10g2(2Z) is achieved, the capacity per bit is 1, the average overlap between different solutions is zero and Zo(K) logK for lar...

A hierarchical neural network model for the identiication of arbitrary contour shapes is presented. Tolerance towards translation, rotation and scaling is achieved far more cost-eeectively than for a fully connected multi-layer perceptron.

We study the performance of a single-layer perceptron in realising a binary mapping of Gaussian input patterns. By introducing non-trivial correlations among the patterns, we generate a family of mappings including easier ones where similar inputs are mapped into the same output, and more difficult ones where similar inputs are mapped into different classes. The difficulty of the problem is gau...