Set Decipherable Languages And Generators
/ Tran Vinh Due
Đầu mục:0
Tài liệu số:1We investigate the problem to characterize whether the infinite product of a given language L is generated by an w-code. Up to now, this problem is open even if language L is a finite language. In this work, we consider a class of languages named w-set decipherable languages winch are veryclose to the w-codes. We solve the problem in the restricted case where L is w-set decipherable and L* is the greatest generator of LB .
Incrementally updating approximation. Incomplete information systems under the Variation of objects / Tran T T Huyen, Le Ba Dung, Nguyen Van Do..[and others]
Đầu mục:0
Tài liệu số:1In covering approximation space, the rough membership functions give numerical charac-terizations of covering-based rough set approximations. It is considered as a tool for establishing the relationship between covering-based rough sets and fuzzy covering-based rough sets. In this paper, we introduce a new method to update the approximation sets with rough membership functions in covering approximation space. Firstly, we present the third types of rough membership functions and study their properties. And then, we consider the change of them while simultaneously adding and removing objects in the information system. Based on that change, we propose a method for upda- ting the approximation sets when the objects vary over time. We proved that the method facilitates knowledge maintenance without retrain from scratch.
Discriminative Dictionary Pair Learning For Image Classification / Nguyen Hoang Vu, Tran Quoc Cuong, Tran Thanh Phong
Đầu mục:0
Tài liệu số:1Dictionary learning (DL) for sparse coding has been widely applied in the field of computer vision. Many DL approaches have been developed recently to solve pattern classification problems and have achieved promising performance. In this paper, to improve the discriminability of the popular dictionary pair learning (DPL) algorithm, we propose a new method called discriminative dictionary pair learning (DDPL) for image classification. To achieve the goal of signal representation and discrimination, we impose the incoherence constraints on the synthesis dictionary and the low- rank regularization on the analysis dictionary. The DDPL method ensures that the learned dictionary has a powerful discriminative ability and signals are more separable after coding. We evaluate the proposed method on benchmark image databases in comparison with existing DL methods. The experimental results demonstrate that our method outperforms many recently proposed dictionary learning approaches.
Some new results on automatic identification of Vietnamese folk songs cheo and quanho
Đầu mục:0
Tài liệu số:1The paper will present an overview of the classification of music genres that have been performed in Vietnam and abroad. For two types of very popular folk songs of Vietnam such as Cheo and Quanho, the paper describes the dataset and Gaussian Mixture Model (GMM) to perform the experiments on identifying some of these folk songs. The GMM used for experiment with 4 sets of parameters containing Mel Frequency Cepstral Coefficients (MFCC), energy, the first and the second derivatives of MFCC and energy, tempo, intensity, and fundamental frequency. The results showed that the parameters added to the MFCCs contributed significantly to the improvement of the identification accuracy with the appropriate values of Gaussian component number M. Our experiments also showed that, on average, the length of the excerpts was only 29.63% of the whole song for Cheo and 38.1% of the whole song for Quanho, the identification rate was only 3.1% and 2.33% less than the whole song for Cheo and Quanho, respectively. The identification of Cheo and Quanho was also tested with i-vectors.
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