In this paper we present three classifiers used in automatic forms class identification. A first category of classifier includes the k-Nearest Neighbours (kNN) and the Multi-Layer Perceptron (MLP) classifiers. A second category corresponds to a new structural classifier based on tree comparison. On one hand, a low level information based on a pyramidal decomposition of the document image is use...

In this paper, we present an application of neural networks in the renewable energy domain. We have developed a methodology for the daily prediction of global solar radiation on a horizontal surface. We use an ad-hoc time series preprocessing and a Multi-Layer Perceptron (MLP) in order to predict solar radiation at daily horizon. First results are promising with nRMSE < 21% and RMSE < 998 Wh/m2...

We propose Very Simple Classifier (VSC) a novel method designed to incorporate the concepts of subsampling and locality in the definition of features to be used as the input of a perceptron. The rationale is that locality theoretically guarantees a bound on the generalization error. Each feature in VSC is a maxmargin classifier built on randomly-selected pairs of samples. The locality in VSC is...

In this study, the performance of two neural classifiers; namely Multi Layer Perceptron (MLP) and Radial Basis Fuction (RBF), are compared for a multivariate classification problem. MLP and RBF are two of the most widely neural network architecture in literature for classification and have successfully been employed for a variety of applications. A nonlinear scaling scheme for multivariate data...

In this paper, after some introductory remarks into the classification problem as considered in various research communities, and some discussions concerning some of the reasons for ascertaining the performances of the three chosen algorithms, viz., CART (Classification and Regression Tree), C4.5 (one of the more recent versions of a popular induction tree technique known as ID3), and a multi-l...

The Q-Credit Assignment (QCA) is a method, based on Q-learning, for allocating credit to rules in Classiier Systems with internal state. It is more powerful than other proposed methods, because it correctly evaluates shared rules, but it has a large computational cost, due to the Multi-Layer Perceptron (MLP) that stores the evaluation function. We present a method for reducing this cost by redu...

The combined use of multi layer perceptron (MLP) and perceptual linear prediction (PLP) features has been reported to improve the performance of automatic speech recognition systems for many different languages and domains. However, MLP features have not yet been used on unsupervised acoustic model training. This approach is introduced in this paper with encouraging results. In addition, unsupe...

This paper proposes the application to the liver fibrosis stadialization of a novel training technique of feed-forward neural networks based on the Bayesian paradigm. Using the Pearson’s r correlation coefficient instead of the standard backpropagation algorithm to update the synaptic weights of a multi-layer perceptron, the proposed model is compared with traditional machine learning algorithm...

This paper is focused on the incorporation of recent techniques for multi-layer perceptron (MLP) based feature extraction in Temporal Pattern (TRAP) and Hidden Activation TRAP (HATS) feature extraction scheme. The TRAP scheme has been origin of various MLP-based features some of which are now indivisible part of state-of-the-art LVCSR systems. The modifications which brought most improvement – ...

In this paper, we present some practical experiments for continuous speech frame-by-frame phoneme classification using Multi Layer Perceptron (MLP) neural networks. We used to train and test our software application, the the OASIS Numbers speech database. In our experiments, we tried to classify all the existing 32 phonemes together, from OASIS Numbers database dictionary. We also used differen...