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 (ANNs) are capable of accurate recognition of simple speech vocabularies such as isolated digits [1]. This paper looks at two more difficult vocabularies, the alphabetic E-set and a set of polysyllabic words. The E-set is difficult because it contains weak discriminants and polysyllables are difficult because of timing variation. Polysyllabic word recognition is aided...

Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences – such as the Baum-Welch algorithm – are available only for generative...

In many applications, high dimensional input data can be considered as sampled functions. We show in this paper how to use this prior knowledge to implement functional preprocessings that allow to consistently reduce the dimension of the data even when they have missing values. Preprocessed functions are then handled by a numerical MLP which approximates the theoretical functional MLP. A succes...

Abstract Deep learning is a promising branch of machine learning. It an algorithm that uses artificial neural networks as the architecture to characterize and learn data. In recent years, many companies, for example, Google, Microsoft Baidu, have become interested in field deep set up large-scale projects, such Google’s Deepmind project, including alphago, which has achieved success Go e-sports...

We introduce tensor product neural networks, composed of a layer of univariate neurons followed by U net of polynomial post-processing. We look at the general approximation problem by these networks observing in particular their relationship to the StoneWeierstrass theorem for uniform function algebras. The implementation of the post-processing as a two-layer network with logarithmic and expone...

This section introduces multilayer perceptrons, which are the most commonly used type of neural network. The popular backpropagation training algorithm is studied in detail. The momentum and adaptive step size techniques, which are used for accelerated training, are discussed. Other acceleration techniques are briefly referenced. Several implementation issues are then examined. The issue of gen...

Rosenblatt's perceptron is extended to (1) a multivalued perceptron and (2) to a continuous-valued perceptron. It shown that any function that can be represented by the multivalued perceptron can be learned in a nite number of steps, and any function that can represented by the continuous perceptron can be learned with arbitrary accuracy in a nite number of steps. The whole apparatus is deened ...

This paper presents a new method for branch prediction. The key idea is to use one of the simplest possible neural networks, the perceptron, as an alternative to the commonly used two-bit counters. Our predictor achieves increased accuracy by making use of long branch histories, which are possible because the hardware resources for our method scale linearly with the history length. By contrast,...