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共 134 条记录，以下是101-110 订阅

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Automatic pattern recognition on archaeological ceramic by 2D and 3D image analysis : A feasibility study 5

Automatic pattern recognition on archaeological ceramic by 2...

引用

international conference on Image Processing theory Tools and Applications

作者： Teddy, Debroutelle Romain, Janvier Aladine, Chetouani Sylvie, Treuillet Matthieu, Exbrayat Lionel, Martin Sebastien, Jesset Univ Orleans Lab PRISME 12 Rue Blois F-45067 Orleans 2 France Univ Orleans LIFO F-45067 Orleans 2 France Serv Archeol Municipal Orleans F-45000 Orleans France

ISBN: (纸本)9781479986378

the ARCADIA project aims at developing automatic image analysis and machine learning methods to facilitate the interpretation and promotion of an archaeological ceramic heritage. It consists in implementing a 3D digitalization chain of the discovered shards to extract the decoration imprinted by the potters using a carved wood wheel. the characterization of the hollow pattern from a binary map given by the 3D image then allows the automatic classification of these patterns. We propose here a feasibility study by comparing the results of automatic classification from the manual inkings made by archaeologists and those from 3D digitalizations of the same corpus of shards.

关键词： Archaeological ceramic heritage carved wood wheel decoration 3D pattern recognition classification

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Multi-scoring feature selection method based on SVM-RFE for prostate cancer diagnosis 5

Multi-scoring feature selection method based on SVM-RFE for ...

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5th international conference on Electrical Engineering and Informatics, ICEEI 2015

作者： Albashish, Dheeb Sahran, Shahnorbanun Abdullah, Azizi. Adam, Afzan Shukor, Nordashima Abd Pauzi, Suria Hayati Md Pattern Recognition Research Group Center for Artificial Intelligence Technology Faculty of Information Science and Technology University Kebangsaan Malaysia Bangi43600 Malaysia Department of Pathology University Kebangsaan Malaysia Medical Center Malaysia

ISBN: (纸本)9781467373197

Prostate cancer diagnosis is based mainly by microscopic evaluation of prostate tissue biopsy which includes assigning cancer grading. the latter is crucial in evaluating the prognosis or cancer progression and treatment. the common grading system used is Gleason grading system that classifies the prostate cancer into five basic grades based on the architecture and pattern of glandular proliferation. However, this process may be subjected to inter and intra observer variation. therefore, the main aim of this paper is to develop a computer aided diagnosis (CAD) utilizing supervised machine learning techniques for Gleason grading of prostate histology. the proposed procedure utilizes the main tissue components of the images in an ensemble style to correctly classify the input histopathological image into benign or malignant. Moreover, the texture features of the benign and malignant images can be used to build the proposed ensemble framework. However, not all extracted texture features contribute to the improvement of the classification performance of the proposed ensemble framework. therefore, to select the more informative features from a set is a critical issue. In this study, a new multi-scoring features selection method based on SVM-RFE and conditional mutual information (CMI) is proposed. © 2015 ieee.

关键词： Grading

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Persian Handwritten Digits recognition by Using Zoning and Histogram Projection

Persian Handwritten Digits Recognition by Using Zoning and H...

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3rd Joint conference of AI and Robotics / 5th RoboCup Iran Open international Symposium

作者： Nooraliei, Amir Islamic Azad Univ Qazvin Branch Dept Elect Comp & IT Engn Qazvin Iran

ISBN: (纸本)9781467363150

In this paper, Persian handwritten digits reorganization by using zoning features and projection histogram for extracting feature vectors with 69-dimensions is presented. In classification stage, support vector machines (SVM) with three linear kernels, polynomial kernel and Gaussian kernel have been used as classifier. We tested our algorithm on the dataset that contained 8600 samples of Persian handwritten digits for performance analysis. Using 8000 samples in learning stage and another 600 samples in testing stage. the results got with use of every three kernels of support vector machine and achieved maximum accuracy by using Gaussian kernel with gamma equal to 0.16. In pre-processing stage only image binarization is used and all the images of this dataset had been normalized at center with size 40x40. the recognition rate of this method, on the test dataset 97.83% and on all samples of dataset 100% was earned.

关键词： pattern recognition Optical character recognition Support vector machine Persian handwritten digits

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An Incremental learning Algorithm for Improved Least Squares Twin Support Vector machine 5th

An Incremental Learning Algorithm for Improved Least Squares...

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ieee 5th international conference on Advanced Computational Intelligence (ICACI)

作者： Yang, Ling Liu, Kai Liang, Xiaodong Ma, Tao Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9781467317443

In this paper, we mainly propose an incremental version of improved least squares twin support vector machine (IILSTSVM), based on inverse matrix-free method. this algorithm can meet the requirement of online learning to update the existing model. In the case of low dimension data, this method effectively improves training speed of incremental learning. According to updating inverse matrix, we can implement the incremental learning for ILSTSVM. Experiments prove that this algorithm has excellent performance on runtime and recognition rate in the low dimensional space.

关键词： inverse problems learning (artificial intelligence) least squares approximations matrix algebra pattern classification support vector machines

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QP representable mahalanobis ellipsoidal learning machine for imbalanced data classification

QP representable mahalanobis ellipsoidal learning machine fo...

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2012 5th international Joint conference on Computational Sciences and Optimization, CSO 2012

作者： Xue, Zhenxia Luo, Juan Liu, Wanli School of Mathematics and Statistics Henan University of Science and Technology Luoyang 471003 China School of Mathematics and Information Science Pingdingshan College Pingdingshan 467000 China School of Mathematics Science Luoyang Normal University Luoyang 471022 China

ISBN: (纸本)9780769546902

In this paper, we propose a convex quadratic programming representable Minimum Mahalanobis Enclosing Ellipsoid (QP-MMEE) for generally imbalanced dataset classification. this algorithm is modified from the previously MMEE algorithm (see paper [X.K. Wei, Y.H. Li, Y. Feng, and ***, "Minimum Mahalanobis enclosing ellipsoid machine for pattern classification, " In Proceeding of the 3th international conference on Intelligent Computing (ICIC'07), CCIS 2, 2007,pp.1176-1185.]) Following the idea of MMEE, this method also tries to seek a hyper-ellipsoid to enclose almost all examples from one class but excludes almost all examples from the other class at the same time. We formulate the MMEE method as a convex quadratic programming problem. To further enhance its performance, a robust version of QP-MMEE is also proposed. We validate the proposed method using real world UCI benchmark datasets. © 2012 ieee.

关键词： pattern recognition

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Epileptic EEG detection via a novel pattern recognition framework

Epileptic EEG detection via a novel pattern recognition fram...

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5th international conference on Bioinformatics and Biomedical Engineering, iCBBE 2011

作者： Song, Yuedong Liò, Pietro Computer Laboratory University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9781424450893

In this paper, we investigate the potential of a recently-developed machine learning algorithm named Extreme learning machine (ELM), together with Sample Entropy (SampEn), to the task of identifying epileptic EEG signals. In order to decrease network complexity of ELM and reduce the subjectivity involved in selecting the number of hidden neurons in ELM network, we propose a structureoptimized strategy for enhancing the ELM algorithm. Experimental results show that the proposed algorithm not only achieves more compact network structure and higher accuracy, but also needs less learning time than conventional ELM and gradient-based algorithms such as Backpropagation Neural Network (BPNN). © 2011 ieee.

关键词： pattern recognition

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Arabic handwritten datasets for pattern recognition and machine learning

Arabic handwritten datasets for pattern recognition and mach...

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2011 5th international conference on Application of Information and Communication Technologies, AICT 2011

作者： Musa, Mohamed E. M. Sudan University of Science and Technology Sudan

ISBN: (纸本)9781612848310

this paper describes the efforts of SUST-ALT (Sudan University of Science and Technology- Arabic Language Technology group) research group towards building datasets for research in recognition of Arabic handwritten. the datasets contain: numerals datasets, isolated Arabic letters datasets, Arabic names datasets. Most of these datasets are off-line. the paper also describes some published results as well as future work. © 2011 ieee.

关键词： learning systems

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pattern recognition of chatter gestation based on hybrid SOM/DHMM architecture

Pattern recognition of chatter gestation based on hybrid SOM...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Kang, Jing Feng, Chang-Jian Shao, Qiang Hu, Hong-Ying Dalian Natl Univ Dept Mech Engn Dalian 116600 Peoples R China

ISBN: (纸本)9781424410651

To distinguish chatter gestation, chatter recognition based on hybrid SOM/DHMM is proposed for dynamic patterns of chatter gestation in cutting process. At first FFT features are extracted from the vibration signal of cutting process, then FFT vectors are presorted and coded into code book of integer numbers by SOM(Self-Organizing Feature Map), and these code books are introduced to DHMM (Discrete Hidden Markov Models), for machine learning and classification. Finally the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

关键词： Discrete Hidden Markov Model (DHMM) chatter Self-Organizing Feature Map(SOM) prediction vector coding

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Research on tool failure prediction and wear monitoring based HMM pattern recognition theory

Research on tool failure prediction and wear monitoring base...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Kang, Jing Kang, Ni Feng, Chang-Jian Hu, Hong-Ying Dalian Natl Univ Dept Mech Engn Dalian 116600 Peoples R China Southeast Univ Dept Instrument Sci & Engn Nanjing 210088 Peoples R China

ISBN: (纸本)9781424410651

A method of pattern recognition of tool wear based on Discrete Hidden Markov Models (DHMM) is proposed to monitor tool wear and to predict tool failure. At the first FFT features are extracted from the vibration signal and cutting force in culling process, then FFT vectors are presorted and coded into code book of integer numbers by SOM, and these code books are introduced to DHMM for machine learning to, build up 3-HMMs for different tool wear stage. And then, pattern is recognised by using maximum probability. Finally the results of tool wear recognition and failure prediction experiments were presented and shown that the method proposed is effective.

关键词： Discrete Hidden Markov Model (DHMM) tool wear pattern recognition prediction Self-Organizing Feature Map(SOM)

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Multiscale wavelet support vector machine for image approximation

Multiscale wavelet support vector machine for image approxim...

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5th international conference on Wavelet Analysis and pattern recognition

作者： Cheng, Hui Lu, Chi Han, Hai Tian, Jin-Wen Jianghan Univ Sch Math & Comp Sci Wuhan 430056 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recongnit & Artificial Intelligence Wuhan 430074 Peoples R China

ISBN: (纸本)9781424410651

In this paper, a new, multiscale wavelet support vector machines model (MWSVM) is proposed, according to the Mercer condition, the wavelet frame theory and kernel function nature. From the statistical learning theory and the SVM model, pointed out the SVM essence is kernel method, the different kernel function has decided the different SVM. the choice of kernel parameters is simplified in MWSVM. By the experiment with the single-variable two-variable function and real image, the new model can approach linear and the non-linear combination functions very well. the experimental result shows that MWSVM is the validity and the usability.

关键词： SVM kernel function wavelet SVR

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