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2011 International Conference on Machine Learning and Cybernetics最新文献

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Text detection in images based on Multiple Kernel Learning 基于多核学习的图像文本检测
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6017013
Shen Lu, Yanyun Qu, Xiaofeng Du, Yi Xie
{"title":"Text detection in images based on Multiple Kernel Learning","authors":"Shen Lu, Yanyun Qu, Xiaofeng Du, Yi Xie","doi":"10.1109/ICMLC.2011.6017013","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6017013","url":null,"abstract":"Detecting text accurately is an essential requirement for text recognition. In this paper, we propose a method to automatically detect text information in images. We firstly find the candidates of text regions based on the analysis of connected components and extract textural features in these candidate regions. We apply Multiple Kernel Learning to train a classifier with an optimal combination of kernels. The classifier can be used to distinguish text from icons which might be included in region candidates. Our method has been successfully implemented in detecting text from the interface images of mobile phones. According to the experimental results, our method outperforms several typical SVM based methods.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116768188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analyze and detect malicious code for compound document binary storage format 分析和检测复合文档二进制存储格式的恶意代码
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6016767
Yubin Gao, De-yu Qi
{"title":"Analyze and detect malicious code for compound document binary storage format","authors":"Yubin Gao, De-yu Qi","doi":"10.1109/ICMLC.2011.6016767","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6016767","url":null,"abstract":"Comparing traditional malicious attack, embedding malicious codes into documents is becoming a more efficient and hidden way. The attackers embed the malicious codes into a document based on the document storage format so that they activate secretively when the document is opened by third-party software. With a simple action of double click the document, it could bring a nightmare to the user. Through researching and analyzing the structure of compound file, we mainly focus on the Word documents, and try to find out a method to detect them. We have used the bloom filter as well as the entropy rate of Markov chain and reached a high accuracy. Detect embedded malicious codes by analyzing the embedded codes themselves, because they are machine instructions which must can execute by CPU. A basic assumption is that the machine instructions in the document are different from the normal text, pictures, tables, etc. The basic direction of detection is to find the different areas in the document. Thus, we use the entropy rate as a measure to quantify this distinction.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125141505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Dynamic threshold algorithm for removal of Back-to-Front noises of visual document image 动态阈值算法去除视觉文档图像的背向噪声
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6017030
Da-Zeng Tian, Chao Wang, Zhiming Zhang
{"title":"Dynamic threshold algorithm for removal of Back-to-Front noises of visual document image","authors":"Da-Zeng Tian, Chao Wang, Zhiming Zhang","doi":"10.1109/ICMLC.2011.6017030","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6017030","url":null,"abstract":"As we all known, the Back-to-Front noises may affect the feature extraction and classification of text when using ORC to identify. In this paper, we take advantage of dynamic threshold method to process the vision document image of Back-to-Front noise with unimodal or bimodal histogram characteristic. Experimental results show that this algorithm has a better effective.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"32 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114001809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Analysis of three methods for web-based opinion mining 基于web的意见挖掘的三种方法分析
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6016768
Haibing Ma, Yibing Geng, Junrui Qiu
{"title":"Analysis of three methods for web-based opinion mining","authors":"Haibing Ma, Yibing Geng, Junrui Qiu","doi":"10.1109/ICMLC.2011.6016768","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6016768","url":null,"abstract":"For the purpose of measuring semantic orientation of documents, we implemented an opinion mining tool which hybrids three different methods: The first one is based on semantic patterns, which simplify the structure of the natural language syntax; the second is based on the weighted sentiment lexicon, which used as semantic feature words; and the third one is based on traditional KNN or SVM text classification method. Our experiments show that each method has its own shorts and advantages.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122823152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
Malicious web page detection based on on-line learning algorithm 基于在线学习算法的恶意网页检测
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6016954
Wen Zhang, Yuxin Ding, Yan Tang, Bin Zhao
{"title":"Malicious web page detection based on on-line learning algorithm","authors":"Wen Zhang, Yuxin Ding, Yan Tang, Bin Zhao","doi":"10.1109/ICMLC.2011.6016954","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6016954","url":null,"abstract":"The Internet has become an indispensable tool in peoples' daily life. It also bring us serious computer security problem. One big security threat comes from malicious webpages. In this paper we study how to detect malicious pages. Since malicious webpages are generated inconstantly, we use on line learning methods to detect malicious webpages. To keep the client side as safe as possible, we do not download the webpages, and analysis webpages' content. We only use URL information to determine if the URL links to a malicious pages. The feature selection methods for URL are discussed, and the performances of different on line learning methods are compared. To improve the performance of on line learning classifiers, an improved on line learning method is proposed, experiments show that this method is effective.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121757672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
Constant speed VSC of a spherical robot driven by Omni wheels 由Omni轮驱动的球形机器人恒速VSC
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6016886
Chi-Hua Wang, Yu-Hsiang Lin, K. Huang, Bore-Kuen Lee, Kuo-Bin Lin, Chi-Kuang Hwang
{"title":"Constant speed VSC of a spherical robot driven by Omni wheels","authors":"Chi-Hua Wang, Yu-Hsiang Lin, K. Huang, Bore-Kuen Lee, Kuo-Bin Lin, Chi-Kuang Hwang","doi":"10.1109/ICMLC.2011.6016886","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6016886","url":null,"abstract":"This paper mainly outlines the model of the invented spherical robot using Omni wheels to drive a spherical wheel. The dynamical model is derived based on Euler Lagrange approach. Therefore, based on the derived model, the variable structure control (VSC) is presented in which the sliding mode control (SMC) is adopted to achieve a constant speed at a vertical balance altitude. Simulations of the proposed control algorithm have been conducted based on two pre-determined sliding surfaces with adjustable parameters to discuss the effective time to enter the sliding surface and the convergence.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127831199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Application of neural network to identify the remote sensing data of hillslide 神经网络在滑坡遥感数据识别中的应用
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6016793
Ting-Shiuan Wang, Teng-To Yu
{"title":"Application of neural network to identify the remote sensing data of hillslide","authors":"Ting-Shiuan Wang, Teng-To Yu","doi":"10.1109/ICMLC.2011.6016793","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6016793","url":null,"abstract":"This study presents the results of neural network simulation of hillside area prediction from remote sensing data. Five neural network methods were compared, which were Back Propagation Network (BPN), Extend Neuron Networks (ENN), Fuzzy Neural Network (FNN), Analysis Adjustment Synthesis Network (AASN), and Genetic Algorithm Neural Network (GANN). Three factors were used as the predictor in this study, which were NDVI value, shape factor, and color difference. The result reveals that the BPN is the best choice, because the error is the lowest among the five schemes in this study.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132828238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Novel algorithms of attribute reduction for variable precision rough set 变精度粗糙集属性约简新算法
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6016740
Yanyan Yang, De-gang Chen, S. Kwong
{"title":"Novel algorithms of attribute reduction for variable precision rough set","authors":"Yanyan Yang, De-gang Chen, S. Kwong","doi":"10.1109/ICMLC.2011.6016740","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6016740","url":null,"abstract":"The main application of variable precision rough set is to perform attribute reduction for databases. In variable precision rough set, the approach of discernibility matrix is theoretical foundation of finding reducts. In this paper, we observe that only minimal elements in the discernibility matrix is sufficient to find reducts, and every minimal element in the discernibility matrix is determined by one equivalence class pair relative to condition attributes at least; this fact motivates our idea in this paper to search the connection between this kind of pair and the minimal element in the discernibility matrix. By the connection between them, we develop the novel algorithms of finding reducts, which improve the existing ones in terms of discernibility matrix.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131862085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
An indoor positioning system based on active RFID in conjunction with Bayesian network 一种结合贝叶斯网络的有源RFID室内定位系统
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6016710
Shou-Hsiung Cheng
{"title":"An indoor positioning system based on active RFID in conjunction with Bayesian network","authors":"Shou-Hsiung Cheng","doi":"10.1109/ICMLC.2011.6016710","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6016710","url":null,"abstract":"This study proposes an indoor positioning system based on active RFID in conjunction with bayesian network. The system can accurately recognizes the locations of active RFID tags by using bayesian network classifiers after the active RFID readers has received different intensity of electromagnetic waves transmitted by active RFID tags. The experimental results show that the proposed system is simple, efficient and useful for practical applications. The proposed indoor positioning system can be utilized in wares locating of storehouse and indoor robot position locating.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132283376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A survey of the initialization of centers and widths in radial basis function network for classification 径向基函数网络分类中中心和宽度初始化的研究
2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI: 10.1109/ICMLC.2011.6016937
Chunru Dong, P. Chan, Wing W. Y. Ng, D. Yeung
{"title":"A survey of the initialization of centers and widths in radial basis function network for classification","authors":"Chunru Dong, P. Chan, Wing W. Y. Ng, D. Yeung","doi":"10.1109/ICMLC.2011.6016937","DOIUrl":"https://doi.org/10.1109/ICMLC.2011.6016937","url":null,"abstract":"The radial basis function network (RBFN) has been widely used in various fields such as function regression, pattern recognition, and error detection, etc. However, the structural parameters of RBFN including the number of hidden units, centers vectors, and widths (variances) are one of the most importent issues when training a RBFN, which greatly affect the performance of RBFN. So, the objective of this paper is to construct an elementary survey about this problem. Firstly, the fundamental knowledge and notations of RBFN is introduced. Secondly, we summarize most existing network structure initialization methods for RBFN and categorize them into four goups. Then some typical appraoches for each category are introduced and discussed. The disadvantages and virtues for parts of methods are also introduced. Finally, the paper is concluded with a discussion of current difficulties and possible future directions about RBFN architecture selection.","PeriodicalId":228516,"journal":{"name":"2011 International Conference on Machine Learning and Cybernetics","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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