Последнее обновление: 25.10.2006   Статьи / Классификаторы

Классификаторы

Combination of multiple classiers using probabilistic dictionary and its application to postcode recognition Авторы: Yue Lu, Chew Lim Tan
Организация: Department of Computer Science, School of Computing, National University of Singapore.
Дата: ориентировочно 2003 год
Кол-во страниц: 10
Combination of multiple classiers is regarded as an eective strategy for achieving a practical system of handwritten character recognition. A great deal of research on the methods of combining multiple classiers has been reported to improve the recognition performance of single characters. However, in a practical application, the recognition performance of a group of characters (such as a postcode or a word) is more signicant and more crucial. With the motivation of optimizing the recognition performance of postcode rather than that of single characters, this paper presents an approach to combine multiple classiers in such a way that the combination decision is carried out at the postcode level rather than at the single character level, in which a probabilistic postcode dictionary is utilized as well to improve the postcode recognition ability. It can be seen from the experimental results that the proposed approach markedly improves the postcode recognition performance and outperforms the commonly used methods of combining multiple classiers at the single character level. Furthermore, the sorting performance of some particular bins with respect to the postcodes with low frequency of occurrence can be improved signicantly at the same time. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
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Combination of Type III Digit Recognizers using the Dempster-Shafer Theory of Evidence Авторы: Catalin I Tomaiand Sargur S. Srihari
Организация: CEDAR, Department of Computer Science and Engineering, Buffalo, NY
Дата: ориентировочно 2003-2005 год
Кол-во страниц: 5
The Dempster-Shafer Theory of Evidence is an established method for combining different sources of information. In this paper we explore ways to improve the combination performance by building a better BPA for each classifier using both “global” and “local classifier information. We propose modifications to two well-known BPA-computation methods to make them better suited for combining Type-III classifiers. We also explore the use of compound hypotheses when a classifier cannot confidently choose between the top two returnedclasses. Experimental tests demonstrate the superiority of some of the approaches proposed here on the numeral recognition problem when combining three different classifiers.
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A PERTURBATION-BASED APPROACH FOR MULTI-CLASSIFIER SYSTEM DESIGN Авторы: V.DI LECCE , G.DIMAURO , A.GUERRIERO , S.IMPEDOVO , G.PIRLO , A.SALZO
Организация: Dipartimento di Ing. Elettronica - Politecnico di Bari - Italy
Дата: 2000 год
Кол-во страниц: 6
This paper presents a perturbation-based approach useful to select the best combination method for a multi-classifier system. The basic idea is to simulate small variations in the performance of the set of classifiers and to evaluate to what extent they influence the performance of the combined classifier. In the experimental phase, the Behavioural Knowledge Space and the Dempster-Shafer combination methods have been considered. The experimental results, carried out in the field of hand-written numeral recognition, demonstrate the effectiveness of the new approach.
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Recognition of Handwritten Numerals Using a Combined Classifier with Hybrid Features Авторы: Kyoung Min Kim , Joong Jo Park , Young Gi Song , In Cheol Kim , and Ching Y. Suen
Организация: CENPARMI, Concordia University, Montreal, Canada Department of Control and Instrumentation Engineering, Gyeongsang National University, Korea Hyundai Information Technology Research Center, Korea Department of Electrical Engineering, Yosu National University, Korea
Дата: 2004 год
Кол-во страниц: 9
Off-line handwritten numeral recognition is a very difficult task. It is hard to achieve high recognition results using a single set of features and a single classifier, since handwritten numerals contain many pattern variations which mostly depend upon individual writing styles. In this paper, we propose a recognition system using hybrid features and a combined classifier. To improve recognition rate, we select mutually beneficial features such as directional features, crossing point features and mesh features, and create three new hybrid feature sets from them. These feature sets hold the local and global characteristics of input numeral images. We also implement a combined classifier from three neural network classifiers to achieve a high recognition rate, using fuzzy integral for multiple network fusion. In order to verify the performance of the proposed recognition system, experiments with the unconstrained handwritten numeral database of Concordia University, Canada were performed, producing a recognition rate of 97.85%.
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