Tuan Trung Nguyen

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Classification systems working on large feature spaces, despite extensive learning, often perform poorly on a group of atypical samples. The problem can be dealt with by incorporating domain knowledge about samples being recognized into the learning process. We present a method that allows to perform this task using a rough approximation framework. We show(More)
In this paper we propose to investigate the concept of robustness of combining operations in fuzzy systems. We use a concept similar to modulus of continuity in approximation theory to characterize robustness of fuzzy logic connectives. Speciically, it is shown that min and max are the most robust &? and _?operations. 1. INTRODUCTION In the majority of(More)
In real life, the degrees of certainty that correspond to one of the same expert can diier drastically, and fuzzy control algoirthms translate these diierent degrees of uncertainty into diierent control strategies. In such situation, it is reasonable to choose a fuzzy control methodology that is the least vulnerable to this kind of uncertainty. We show that(More)
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Optical Character Recognition (OCR) is a classic example of decision making problem where class identities of image objects are to be determined. This concerns essentially of finding a decision function that returns the correct classification of input objects. This paper proposes a method of constructing such functions using an adaptive learning framework,(More)
Traditional data mining techniques mainly deal with a search for patterns in traditional-type databases, where data consists of numbers and words. In many application areas, however, data is more complicated: real-life data often comes not as a result of measuring a few quantities, but rather as an image from a camera, and this image can also change(More)