We have applied Bayesian regularisation methods to multi-layer perceptron (MLP) training in the context of a hybrid MLP– HMM (hidden Markov model) continuous speech recognition system. The Bayesian framework adopted here allows an objective setting of the regularisation parameters, according to the training data. Experiments were carried out on the ARPA Resource Management database.

In this paper we show how the robustness of multi-stream multi-layer perceptron (MLP) acoustic models can be increased through uncertainty propagation and decoding. We demonstrate that MLP uncertainty decoding yields consistent improvements over using minimum mean square error (MMSE) feature enhancement in MFCC and RASTA-LPCC domains. We introduce as well formulas for the computation of the unc...

In this paper, we present our recent progress on multi-layer perceptron (MLP) based data-driven feature extraction using improved MLP structures. Four-layer MLPs are used in this study. Different signal processing methods are applied before the input layer of the MLP. We show that the first hidden layer of a four-layer MLP is able to detect some basic patterns from the time-frequency plane. KLT...

Classification of heart sound signals to normal or their classes of disease are very important in screening and diagnosis system since various applications and devices that fulfilling this purpose are rapidly design and developed these days. This paper states and alternative method in improving classification accuracy of heart sound signals. Standard and improvised Multi-Layer Perceptron (MLP) ...

A multilayer perceptron is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separ...

This study uses a new data visualization method, developed by the first author, to investigate the reliability of a real world low-back-pain Multi-layer Perceptron (MLP) network from a hidden layer decision region perspective. Using decision region identification information from an explanation facility, the MLP training examples are discovered to occupy decision regions in contiguous class thr...

It is difficult to train a multi-layer perceptron (MLP) when there are only a few labeled samples available. However, by pretraining an MLP with vast amount of unlabeled samples available, we may achieve better generalization performance. Schulz et al. (2012) showed that it is possible to pretrain an MLP in a less greedy way by utilizing the two-layer contractive encodings, however, with a cost...

This paper compares, for a microfinance institution, the performance of two individual classification models: Logistic Regression (Logit) and Multi-Layer Perceptron Neural Network (MLP), to evaluate the credit risk problem and discriminate good creditors from bad ones. Credit scoring systems are currently in common use by numerous financial institutions worldwide. However, credit scoring using ...