Aim to the automatic recognition problem of acoustic target under the complex background, a kind of fuzzy, patternrecognition system based on the fuzzy synthesis judgement is designed. At the same time, the realizati...
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ISBN:
(纸本)7506249766
Aim to the automatic recognition problem of acoustic target under the complex background, a kind of fuzzy, patternrecognition system based on the fuzzy synthesis judgement is designed. At the same time, the realization technology of the principle prototype is discussed.
We compared the distribution of methylation of histone H3 lysine 4 on genes of different lengths. the result shows that the mono- and di-methylation present different distribution patterns on genes with different leng...
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ISBN:
(纸本)9781424447138
We compared the distribution of methylation of histone H3 lysine 4 on genes of different lengths. the result shows that the mono- and di-methylation present different distribution patterns on genes with different lengths. Furthermore, these two methylations exhibit distinct correlations with expression when gene length is different. Our results suggest that the gene length might play a role in the effect of these two modifications on transcription.
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.
patterns in normal abdominal movement captured with medical imaging can be recognised by a trained radiologist but the process is time consuming. Abdominal adhesions present a diagnostic problem in which the radiologi...
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ISBN:
(纸本)9783642040306
patterns in normal abdominal movement captured with medical imaging can be recognised by a trained radiologist but the process is time consuming. Abdominal adhesions present a diagnostic problem in which the radiologist is asked to detect abnormal movement that may be indicative of pathology. this paper postulates that the use of image analysis call augment the diagnostic abilities of the radiologist in respect of adhesions. Proof of concept experiments were conducted in-silico to explore the effectiveness of the technique. the results indicate that trained participants are accurate in their assessment of abnormalities when supplied with additional information from image analysis techniques. However without the additional information, participants made incorrect diagnoses on many occasions. ROC methods were used to quantify the outcomes of the in-silico experiment.
Binary fingerprints encoding the presence of 2D fragment substructures in molecules are extensively used for similarity-based virtual screening in the agrochemical and pharmaceutical industries. this paper describes t...
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ISBN:
(纸本)9783642040306
Binary fingerprints encoding the presence of 2D fragment substructures in molecules are extensively used for similarity-based virtual screening in the agrochemical and pharmaceutical industries. this paper describes two techniques for enhancing the effectiveness of screening: the use of a second-level search based on the nearest neighbours of the initial reference structure;and the use of weighted fingerprints encoding the frequency of occurrence, rather than just the mere presence, of substructures. Experiments using several databases for which both structural and bioactivity data are available demonstrate the effectiveness of these two approaches.
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.
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.
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.
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