We present a two-class pattern recognition method through the majority vote which is based on weak classifiers. The weak classifiers are defined‘in terms of rectangular regions formed by the original training data. Tests on real and simulated data sets show that this classifier combination procedure can lead to a high accuracy.

Article history: Received 28 August 2012 Received in revised form 4 February 2013 Accepted 8 February 2013 Available online 5 March 2013

This paper presents a novel technique for automated learning from observations. The technique arranges in a row four traditional pattern recognition approaches (numeric, logic, statistical and finally syntactic) within a unifying framework. Each processing step is conceived as a transformation of the input dataset from one state to another. The proposed technique considers measurable observatio...

This paper surveys trend analysis methods and their applications in process industry. Basic principles of several methods are presented together with known applications in process monitoring, diagnosis and control. In the first place, the methods presented analyse process measurements or some calculated quantities as time series using various pattern recognition methods. Also computationally mu...

This paper describes a new framework using intelligent agents for pattern recognition. A justiication for using alternatives to current classiier systems is given. The use of the framework, called iart, is tested on a digit recognition system.

On the basis of documents analysis, this article constructed the sustainable development evaluation model of real estate market from four aspects, including the harmonious development with local economy, reasonable prices, structural balance and financial coordination. Then we empirical studied the healthy development of Nanchang real estate market based on fuzzy pattern recognition model. The ...

a Departamento de Inform atica, Escola Superior de Enxener ıa Inform atica, Universidad de Vigo, Campus Universitario As Lagoas, E-32004 Ourense, Spain b Departamento de Inform atica, Escuela Polit ecnica, Universidad de Extremadura, Avda de la Universidad, s/n, E-10004 C aceres, Spain c Departamento de Zootecnia, Facultad de Veterinaria, Universidad de Extremadura, Avda de la Universidad, s/n,...

The present study describes a model which, by using pattern recognition procedures, detects defects on the external surface of pieces and industrial products, especially those whose borders are indefinite. In addition to describing the model, a practical application and the results obtained are described, including an important extension of this work, i.e. the comparison of pieces with standard...

Manual stratigraphic interpretation of modern 3-D seismic images is extremely timeconsuming. We present a method based on nonstationary predictive signal/noise separation for automatically recognizing the occurence of a predefined pattern in seismic images. The method is tested on 2-D synthetic and real seismic images, and is shown to reliably detect the presence of unconformities in both.