To enhance recognition accuracy of isolated words identification with small samples in lipreading, SVM is first introduced to act as classifier in this paper. As SVM is based on structural risk minimization, it solves...
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ISBN:
(纸本)9781424447138
To enhance recognition accuracy of isolated words identification with small samples in lipreading, SVM is first introduced to act as classifier in this paper. As SVM is based on structural risk minimization, it solves the problem of patternrecognition under small samples, on the other hand, it avoids the unreasonable hypothesis in traditional classifier. To meet the requirement of fixed input feature dimensionality in SVM, several input feature dimensionality normalization methods were discussed and compared, including 3-4-3 data segmenting method, HMM based method and DTAK(Dynamic Time Alignment Kernel) based method. Two experiments were performed on the bimodal database, In the first experiment different input feature normalization algorithm were compared on SVM. Experiments showed that DTAK based normalization achieved the best result, in the second experiments SVM was compared with HMM under different number samples occasion. Experimental results showed that SVM performs better than HMM under small samples.
In this paper, we address the problem of defining and modeling the handwriting signal using its geometrical and spatio-temporal features, in order to improve the recognition task. We use the frequent pattern methods t...
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ISBN:
(纸本)9781538652398
In this paper, we address the problem of defining and modeling the handwriting signal using its geometrical and spatio-temporal features, in order to improve the recognition task. We use the frequent pattern methods to enhance the quality of the signature vector extracted from the handwritten character. Two types of frequent patterns are employed to represent the handwritten characters pertinently: the maximal and closed frequent patterns. We created a new database that contains words of two different letters. the generated results are very promising, through which we have demonstrated that the "minimum threshold", which is an essential parameter in the frequent patterns mining algorithms, represent a key feature in the characters description.
Support vector machines (SVM) are learning algorithms derived from statistical learning theory. the SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-c...
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Support vector machines (SVM) are learning algorithms derived from statistical learning theory. the SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-class classification problems are discussed, in particular we consider binary trees of SVMs to solve the multi-class problem. Numerical results for different classifiers on a benchmark data set of handwritten digits are presented.
the possibility of application of the knowledge base technology to solve the patternrecognition problems has been considered. For this purpose. the investigations to deepen the pilotage information analysing and proc...
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the possibility of application of the knowledge base technology to solve the patternrecognition problems has been considered. For this purpose. the investigations to deepen the pilotage information analysing and processing have been carried out and the tasks related to pilotage technique controlling and assessment have been singled out. Prospects of the application of the fuzzy set theory methods and the decision making using the knowledge about problem domain is demonstrated. (C) 2000 Elsevier Science Ltd. All rights reserved.
Following the fourth edition of the workshop on Reproducible Research in patternrecognition (RRPR) at the internationalconference on patternrecognition (ICPR), this paper reports the main discussions that were held...
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recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, Le., short regulatory DNA sequences l...
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ISBN:
(纸本)9781424417391
recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, Le., short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. In this paper, a machine learning method, called Support Vector Machine (SVM), is used for classification of DNA sequences and promoter recognition. For optimal classification, 11 rules for mapping of DNA sequences into binary SVM feature space are analyzed. Classification is performed using a power series kernel function. Kernel parameters are optimized using a modification of the Nelder-Mead (downhill simplex) optimization method. the results of classification for drosophila and human sequence datasets are presented.
this paper investigates the effect of various feature extraction methods on the recognition ability of a self-organising neural network called Paradise when applied to the problems of the classification of face images...
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this paper investigates the effect of various feature extraction methods on the recognition ability of a self-organising neural network called Paradise when applied to the problems of the classification of face images and hand written character recognition. the feature extraction methods investigated are, oriented Gaussian filters, Gabor filters and oriented Laplacian of Gaussian (LoG) filters. the recognition results for the two applications are Shown to compare favourably with other techniques designed specifically for the two tasks. (C) 2000 Civil-Comp Ltd. and Elsevier Science Ltd. All rights reserved.
One-class classification, which was tested successfully in unbalanced sample classification problems, is one of the hotspots in patternrecognition research. this paper first analysis the commonly used one-class class...
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ISBN:
(纸本)9781728136608
One-class classification, which was tested successfully in unbalanced sample classification problems, is one of the hotspots in patternrecognition research. this paper first analysis the commonly used one-class classification methods, then classifies these methods into three categories: boundary based method, re-construction based method and border based method. Finally, a series of testing experiments based on artificial database are designed to test the advantages and disadvantages of these methods from the aspect of learning ability, classification decision and algorithm complexity.
Face recognition is an important subject because of its usefulness in many applications like banking system and door access control systems. For this reason, there is always the need to recognize faces automatically w...
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the patternrecognition problem in RICH counters concerns the identification of an unknown number of imperfect roughly-circular rings made of a low number of discrete points in presence of background. In this paper we...
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the patternrecognition problem in RICH counters concerns the identification of an unknown number of imperfect roughly-circular rings made of a low number of discrete points in presence of background. In this paper we present some preliminary results obtained using the Possibilistic C-Spherical Shell algorithm. In particular, we show that the algorithm is very tolerant and robust to noise (outliers rate) level. Moreover, for complex images full of rings, we introduce an iterative scheme that greatly improves performances. Besides that, the rings are not requested to be complete, only arcs are enough to recognize the underlying rings by the algorithm.
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