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检索条件"任意字段=5th International Conference on Pattern Recognition in Bioinformatics"

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On Utilizing Optimal and Information theoretic Syntactic Modeling for Peptide Classification

On Utilizing Optimal and Information Theoretic Syntactic Mod...

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4th international conference pattern recognition in bioinformatics

作者： Aygun, Eser Oommen, B. John Cataltepe, Zehra Istanbul Tech Univ Dept Comp Eng TR-80626 Istanbul Turkey Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada Univ Agder Kristiansand Norway Istanbul Tech Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9783642040306

Syntactic methods in pattern recognition have been used extensively in bioinformatics, and in particular, in the analysis of gene and protein expressions, and in the recognition and classification of biosequences, these methods are almost universally distance-based. this paper concerns the use of an Optimal and Information theoretic (OIT) probabilistic model [11] to achieve peptide classification using the information residing in their syntactic representations. the latter has traditionally been achieved using the edit distances required in the respective peptide comparisons. We advocate that, one can model the differences between compared strings as a mutation model consisting of random Substitutions, Insertions and Deletions (SID) obeying the OIT model. thus, in this paper, we show that the probability measure obtained. from the OIT model can be perceived as a sequence similarity metric, using which a Support Vector Machine (SVM)-based peptide classifier, referred to as OIT-SVM, can be devised. the classifier, which we have built has been tested for eight different "substitution" matrices and for two different data sets, namely, the HIV-1 Protease Cleavage sites and the T-cell Epitopes. the results show that the OIT model performs significantly better than the one which uses a Needleman-Wunsch sequence alignment score, and the peptide classification methods that previously experimented with the same two datasets.

关键词： Biological Sequence Analysis Optimal and Information theoretic Syntactic Classifcation Peptide Classification Sequence Processing Syntactic pattern recognition

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Web Application System of Handwritten Text recognition 5

Web Application System of Handwritten Text Recognition

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5th international conference on Computational Linguistics and Intelligent Systems (COLINS)

作者： Bodnia, Yevhen Kozulia, Mariia Natl Tech Univ Kharkiv Polytech Inst Kirpichova Str 2 Kharkiv Ukraine

the problem of text recognition is becoming increasingly important due to the active introduction of digital computing and the widespread use of word processors. pattern recognition is one of the most difficult from a mathematical point of view and one of the most popular areas of artificial intelligence programming. In the work is researched approaches and methods of solving text recognition problem, improved the performance of the available algorithms for text recognition and created algorithmic software. According to the analysis, neural networks were selected for handwriting recognition. the main advantage of using neural networks is a good generalization ability, the ability to use context analysis and recognize a symbol based on the surrounding symbols. the software implementation features of Hopfield and convolutional neural network, genetic algorithm, which were chosen as effective methods for recognizing handwritten text, were considered. Algorithmic software and web application that uses these methods for the task of handwritten text recognition is developed.

关键词： Handwritten text recognition pattern recognition recognition methods neural network genetic algorithm convolutional neural network Hopfield neural network machine learning data models

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On the possibility of objective identification of human vertebrae through pattern recognition algorithms

On the possibility of objective identification of human vert...

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Proceedings of the 5th international conference on Image Processing and its Applications

作者： Inesta, J.M. Sarti, M.A. Buendia, M. Univ Jaume I Spain

the task of finding whether vertebral levels of the human spine can be mathematically differentiated is posed, using a morphometric database of the vertebral quantitative morphology as training set. there is no certainty in the medical community about this possibility. the axial projections of a number of vertebrae were digitized and their features quantified by automatic image analysis algorithms. these measurements build a spinal morphometric database. After the application of feature selection procedures, the selected measurements are used as inputs to different pattern recognition algorithms try to find whether vertebral levels can be distinguished. We have proved the capability to fulfill this classification with a small degree of uncertainty. Artificial neural networks, in particular, have shown their capability to perform well in this difficult pattern recognition task.

关键词： pattern recognition

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Dovetailing of Acoustics and Prosody in Spontaneous Speech recognition 5

Dovetailing of Acoustics and Prosody in Spontaneous Speech R...

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5th international conference on Spoken Language Processing, ICSLP 1998

作者： Buckow, J. Batliner, A. Huber, R. Nöth, E. Warnke, V. Niemann, H. University of Erlangen-Nuremberg Pattern Recognition Martensstr. 3 Erlangen91058 Germany

Prosody can be applied to improve the performance of spontaneous speech translation systems like VERBMOBIL. In VERBMOBIL we previously augmented the output of a word recognizer with prosodic information. Here we present a new approach of interleaving word recognition and prosodic processing. While we still use the output of a word recognizer to determine phrase boundaries, we do not wait until the end of the utterance before we start processing. Instead we intercept chunks of word hypotheses during the forward search of the recognizer. Neural networks and language models are used to predict phrase boundaries. those boundary hypotheses, in turn, are used by the recognizer to cut the stream of incoming speech into syntactic-prosodic phrases. thus, incremental processing is possible. We investigate which features are suited for incremental prosodic processing and compare them w.r.t. classification performance and efficiency. We show that with a set of features that can be computed efficiently classification results are achieved which are almost as good as those with the previously used computationally more expensive features. © 1998. 5th international conference on Spoken Language Processing, ICSLP 1998. All rights reserved.

关键词： Speech recognition

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Unconstrained handwritten numeral recognition using Hausdorff distance and multi-layer neural network classifier 5

Unconstrained handwritten numeral recognition using Hausdorf...

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5th international conference on Document Analysis and recognition, ICDAR 1999

作者： Wu, Xuejing Shi, Pengfei Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200030 China

ISBN: (纸本)0769503187

A method for zip code recognition is presented. 2D binary images are input to HAVNET, a neural network which employs the Hausdorff distance as a similarity metric to train the weights which are required to represent the patterns learned by the network. A new learning rule for HAVNET is also introduced. In this approach, HAVNET is combined with a multilayer neural network. the Hausdorff distance acts as a zip code filter. Only the confusing digits are input into the multilayer network for training or recognition. Experimental results show that the correct-recognition rate with zero rejection is more than 97% for a database used by the Chinese mail sorting system. © 1999 IEEE.

关键词： Multilayer neural networks

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pattern recognition of super-alloy friction welding joint defect by wavelet packet and wavelet neural network

Pattern recognition of super-alloy friction welding joint de...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Yin, Xin Zhang, Zhen Liu, Ai-Min Li, Qi-Lin Zhengzhou Inst Aeronaut Ind Management Dept Mech & Elect Engn Zhengzhou 450015 Peoples R China

ISBN: (纸本)9781424410651

Detecting the super-alloy friction welding specimens of GH4169 by, using the UltraPAC system, aiming at the extracting the defect eigenvalue by using wavelet packet analysis and pattern recognition by making use of the wavelet neural network is discussed this method can realize to extract the interrelated information which can reflect defect characteristic from the ultrasonic information being detected and analysis it by the information. Constructing the network model for realizing the qualitative recognition of defects which is improved though experiment finally. the results show that the wavelet packet analysis adequately make use of the information in time-domain and in frequency-domain of the defected echo signal, multi-level partition the frequency bands and analyze the high-frequency part further which don't been subdivided by multi-resolution analysis, and choose the interrelated frequency bands to make it suited with signal spectrum. thus, the time-frequency resolution is rising, the good local amplificatory property of the wavelet neural network and the study characteristic of multi-resolution analysis can achieve the higher accuracy rate of the qualitative classification of welding defect.

关键词： super-alloy friction welding defect wavelet packet wavelet neural network

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On Probabilistic combination of face and gait cues for identification

On Probabilistic combination of face and gait cues for ident...

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5th IEEE international conference on Automatic Face and Gesture recognition

作者： Shakhnarovich, G Darrell, T MIT Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)0769516025

We approach the task of person identification based on face and gait cites. the cites are derived from multiple simultaneous camera views, combined through the visual hull algorithm to create imagery in canonical pose prior to recognition. these view-normalized sequences, containing frontal images of face and profile silhouettes, are separately used for face and gait recognition, and the results may be combined using a range of strategies. We discuss the issues of cross-modal correlation and score transformations for different modalities, present the probabilistic settings for the cross-modal fusion. and explore several common fusion approaches. the effectiveness of various strategies is evaluated on a data set with 26 subjects. We hope that the discussion presented in this paper may be useful in developing further statistical framework for multi-modal recognition.

关键词： pattern recognition

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Pair-wise Diversity Measure for Multiple Classifier System Based on Evidence theory

Pair-wise Diversity Measure for Multiple Classifier System B...

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5th international conference on Digital Home (ICDH)

作者： Lan, Yuan-Dong Chen, Qun HuiZhou Univ Comp Sci Dept Huizhou Peoples R China HuiZhou Univ Phys Educ Dept Huizhou Peoples R China

ISBN: (纸本)9781479942848

A method of designing pattern recognition systems, known as the multiple classifier system, has emerged over recent years to address the practical problem of designing classification systems with improved accuracy and efficiency. the aim is to design a composite system that outperforms any individual classifier by pooling together the decisions of all classifiers. Diversity among base classifiers is known to be a necessary condition for improvement in multiple classifier system ensemble performance. In this paper, a new method of diversity measure for multiple classifier system is proposed and a new approach to multiple classifier system design is presented. For a given test sample, the diversity measure for each pair of sub-classifier is constructed based on the distance of basic probability assignment. the average value of all pair-wise classifiers is the final result of diversity measure. We combine the proposed new method of diversity measure with the traditional diversity measure, constructing an effective multiple classifier system. Experiments on UCI data sets and SAR images data sets show that the proposed diversity measure and the proposed approach to construct multiple classifier system are rational and effective.

关键词： multiple classifiers system diversity measure ensembles pattern recognition

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Emerging Intelligent Computing Technology and Applications 2012

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丛书名： Communications in Computer and Information Science

2012年

作者： De-Shuang Huang Phalguni Gupta Xiang Zhang Prashan Premaratne

ISBN: (数字)9783642318375

ISBN: (纸本)9783642318368

this book constitutes the refereed proceedings of the 8th international conference on Intelligent Computing, ICIC 2012, held in Huangshan, China, in July 2012. the 242 revised full papers presented in the three volumes LNCS 7389, LNAI 7390, and CCIS 304 were carefully reviewed and selected from 753 submissions. the papers in this volume (CCIS 304) are organized in topical sections on Neural Networks; Particle Swarm Optimization and Niche Technology; Kernel Methods and Supporting Vector Machines; Biology Inspired Computing and Optimization; Knowledge Discovery and Data Mining; Intelligent Computing in bioinformatics; Intelligent Computing in pattern recognition; Intelligent Computing in Image Processing; Intelligent Computing in Computer Vision; Intelligent Control and Automation; Knowledge Representation/Reasoning and Expert Systems; Advances in Information Security; Protein and Gene bioinformatics; Soft Computing and Bio-Inspired Techiques in Real-World Applications; Bio-Inspired Computing and Applications.

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Ideal chaotic pattern recognition using the modified adachi neural network 5

Ideal chaotic pattern recognition using the modified adachi ...

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5th international conference on Chaotic Modeling and Simulation, CHAOS 2012

作者： Qin, Ke Oommen, B.J. University of Electronic Science and Technology of China Chengdu611731 China Carleton University OttawaONK1S 5B6 Canada University of Agder Postboks 509 Grimstad4898 Norway

the benchmark of a chaotic pattern recognition (PR) system is the following: First of all, one must be able to train the system with a set of "training" patterns. Subsequently, as long as there is no testing pattern, the system must be chaotic. However, if the system is, thereafter, presented with an unknown testing pattern, the behavior must ideally be as follows. If the testing pattern is not one of the trained patterns, the system must continue to be chaotic. As opposed to this, if the testing pattern is truly one of the trained patterns (or a noisy version of a trained pattern), the system must switch to being periodic, with the specific trained pattern appearing periodically at the output. this is truly an ambitious goal, with the requirement of switching from chaos to periodicity being the most demanding. the Adachi Neural Network (AdNN) [1-5] has properties which are pseudo-chaotic, but it also possesses limited PR characteristics. As opposed to this, the Modified Adachi Neural Network (M-AdNN) proposed by Calitoiu et al [7], is a fascinating NN which has been shown to possess the required periodicity property desirable for PR applications. In this paper, we shall tune the parameters of the M-AdNN for its weights, steepness and external inputs, to yield a new NN, which we shall refer to as the Ideal-M-AdNN. Using a rigorous Lyapunov analysis, we shall analyze the chaotic properties of the Ideal-M-AdNN, and demonstrate its chaotic characteristics. thereafter, we shall verify that the system is also truly chaotic for untrained patterns. But most importantly, we demonstrate that it is able to switch to being periodic whenever it encounters patterns with which it was trained. Apart from being quite fascinating, as far as we know, the theoretical and experimental results presented here are both unreported and novel. Indeed, we are not aware of any NN that possesses these properties. © 2012 5th Chaotic Modeling and Simulation international conference All Rights Res

关键词： Neural networks

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