Many constructive methods use the pocket algorithm as a basic component in the training of multilayer perceptrons. This is mainly due to the good properties of the pocket algorithm confirmed by a proper convergence theorem which asserts its optimality. Unfortunately the original proof holds vacuously and does not ensure the asymptotical achievement of an optimal weight vector in a general situa...

Semantic annotation influence on coreference detection using perceptron approach The ConLL-2011/2012 evaluation campaign was dedicated to coreference detection systems. This paper presents the coreference resolution system Poly-co submitted to the closed track of the CoNLL-2011 Shared Task and evaluate is potential of evolution when it includes a semantic feature. Our system integrates a multil...

Recent studies in neurobiology and especially in neuroimaging report that a gating mechanism prior to face processing levels of human visual system, facilitates the face/nonface recognition task. In accordance to these biological evidences, we propose a face/nonface recognition model which makes use of mixture of experts network. In order to improve the face/nonface recognition accuracy, the ou...

This paper presents a novel algorithm for the extraction of the eye and mouth (facial features) "elds from 2-D gray-level face images. The fundamental philosophy is that eigenfeatures, derived from the eigenvalues and eigenvectors of the binary edge data set constructed from the eye and mouth "elds, are very good features to locate these "elds e$ciently. The eigenfeatures extracted from the pos...

Multi-layer perceptrons (MLPs) have been widely used in classification and regression task. How to improve the training speed of MLPs has been an interesting field of research. Instead of the classical method, we try to train MLPs by a MiniMin model which can ensure that the weights of the last layer are optimal at each step. Significant improvement on training speed has been made using our met...

This paper describes a technique called Input Reconstruction Reliability Estimation (IRRE) for determining the response reliability of a restricted class of multi-layer perceptrons (MLPs). The technique uses a network's ability to accurately encode the input pattern in its internal representation as a measure of its reliability. The more accurately a network is able to reconstruct the input pat...

This work uses Machine Learning techniques and other classical approaches to analyze both physiological variables and treatment characteristics in patients undergoing chronic renal failure. Firstly, the use of Self-Organizing Maps is proposed in order to extract qualitative knowledge. Secondly, the Hemoglobin concentration is predicted one-month ahead by models based on the Multilayer Perceptro...

This paper is intended to advertise the public availability of the articulatory feature (AF) classification multi-layer perceptrons (MLPs) which were used in the Johns Hopkins 2006 summer workshop. We describe the design choices, data preparation, AF label generation, and the training of MLPs for feature classification on close to 2000 hours of telephone speech. In addition, we present some ana...

In this paper, we will describe several extensions to our earlier work, utilizing a segment-based approach. We will formulate our segmental framework and report our study on the use of multi-layer perceptrons for detection and classification of phonemes. We will also examine the outputs of the network, and compare the network performance with other classifiers. Our investigation is performed wi...