In medical filed, predicting the occurrence of heart diseases is a significant piece work. Millions healthcare-related complexities that have remained unsolved up until now can be greatly simplified with help machine learning. The proposed study concerned cardiac disease diagnosis decision support system. An OpenML repository data stream 1 million instances and 14 features used for this study. ...

Artificial neural networks (ANN), esp. multilayer perceptrons (MLP) have been widely used in pattern recognition and classification. Nevertheless, how to incorporate a priori knowledge to design ANN is still an open problem. This paper tries to give some insightful discussions on this topic emphasizing weight initialization from three perspectives. Theoretical analyses and simulations are offer...

A general framework for similarity-based (SB) classification methods is presented. Neural networks, such as the Radial Basis Function (RBF) and the Multilayer Perceptrons (MLPs) models, are special cases of SB methods. Many new versions of minimal distance methods are derived from this framework.

This paper presents a new learning scheme for improving generalization of multilayer perceptrons. The algorithm uses a multi-objective optimization approach to balance between the error of the training data and the norm of network weight vectors to avoid over"tting. The results are compared with support vector machines and standard backpropagation. ( 2000 Elsevier Science B.V. All rights reserved.

The present thesis introduces Clifford Algebra as a framework for neural computation. Clifford Algebra subsumes, for example, the reals, complex numbers and quaternions. Neural computation with Clifford algebras is model–based. This principle is established by constructing Clifford algebras from quadratic spaces. Then the subspace grading inherent to any Clifford algebra is introduced, which al...

A general framework for minimal distance methods is presented. Radial Basis Functions (RBFs) and Multilayer Perceptrons (MLPs) neural networks are included in this framework as special cases. New versions of minimal distance methods are formulated. A few of them have been tested on a real-world datasets obtaining very encouraging results.

A new perceptron learning rule which works with multilayer neural networks made of multi-state units is obtained, and the corresponding convergence theorem is proved. The deenition of perceptron of maximal stability is enlarged in order to include these new multi-state perceptrons, and a proof of existence and uniqueness of such optimal solutions is outlined.