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learning to Extract Web News Title in Template Independent Way

Learning to Extract Web News Title in Template Independent W...

引用

4th international conference on Rough Sets and Knowledge Technology (RSKT)

作者： Wang, Can Wang, Junfeng Chen, Chun Lin, Li Guan, Ziyu Zhu, Junyan Zhang, Cheng Bu, Jiajun Zhejiang Univ Coll Comp Sci Hangzhou Zhejiang Peoples R China China Disabled Persons Fed Informat Ctr Beijing Peoples R China

ISBN: (纸本)9783642029615

Many news sites have large collections of news pages generated dynamically and endlessly from underlying databases. Automatic extraction of news titles and contents from news pages therefore is air important technique for applications like news aggregation systems. However, extracting news titles accurately from news pages of various styles is found to be a challenging task in previous work. In this paper, we propose a machine learning approach to tackle this problem. Our approach is independent of templates and thus will riot suffer from the updates of templates which usually invalidate the corresponding extractors. Empirical evaluation of our approach over 5,200 news Web pages collected from 13 important on-line news sites shows that our approach significantly improves the accuracy of news title extraction.

关键词： data extraction Web mining Web news

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Sparse kernel learning and the relevance units machine

Sparse kernel learning and the relevance units machine

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13th Pacific-Asia conference on Knowledge Discovery and data mining, PAKDD 2009

作者： Gao, Junbin Jun, Zhang School of Accounting and Computer Science Charles Sturt University Bathurst NSW 2795 Australia Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China

ISBN: (纸本)3642013066

the relevance vector machine(RVM) is a state-of-the-art constructing sparse regression kernel model [1,2,3,4]. It not only generates a much sparser model but provides better generalization performance than the standard support vector machine (SVM). In RVM and SVM, relevance vectors (RVs) and support vectors (SVs) are both selected from the input vector set. this may limit model flexibility. In this paper we propose a new sparse kernel model called Relevance Units machine (RUM). RUM follows the idea of RVM under the Bayesian framework but releases the constraint that RVs have to be selected from the input vectors. RUM treats relevance units as part of the parameters of the model. As a result, a RUM maintains all the advantages of RVM and offers superior sparsity. the new algorithm is demonstrated to possess considerable computational advantages over well-known thestate-of-the-art algorithms. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Support vector machines

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Supporting Diagnostics of Coronary Artery Disease with Multi-resolution Image Parameterization and data mining

Supporting Diagnostics of Coronary Artery Disease with Multi...

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4th international conference on Computer Vision/Computer Graphics Collaboration Techniques (MIRAGE 2009)

作者： Kukar, Matjaz Sajn, Luka Univ Ljubljana Fac Comp & Informat Sci SI-1001 Ljubljana Slovenia

ISBN: (纸本)9783642018107

Coronary artery disease has been described as one of the curses of the western world, as it is one of the most important causes of mortality. therefore, clinicians seek to improve diagnostic procedures, especially those that allow them to reach reliable early diagnoses. In the clinical setting, coronary artery disease diagnostics is typically performed in a stepwise manner. the four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and ECG (electrocardiogram) at rest, (2) sequential ECG testing during the controlled exercise, (3) myocardial perfusion scintigraphy, and finally (4) coronary angiography, that is considered as the "gold standard" reference method. Our study focuses on improving diagnostic performance of the third diagnostic level. Myocardial scintigraphy is non invasive;it results in a series of medical images that are relatively inexpensively obtained. In clinical practice, these images are manually described (parameterized) by expert physicians. In the paper we present an innovative alternative to manual image evaluation - an automatic image parameterization in multiple resolutions, based on texture description with specialized association rules. Extracted image parameters are combined into more informative composite parameters by means of principle component analysis, and finally used to build automatic classifiers with machine learning methods. Our experiments with synthetic datasets show that association-rule-based multi-resolution image parameterization equals or surpasses other state-of-the-art methods for finding multiple informative resolutions. Experimental results in coronary artery disease diagnostics confirm these results as our approach significantly improves the clinical results in terms of quality of image parameters as well as diagnostic performance.

关键词： machine learning coronary artery disease medical diagnostics multi-resolution image parameterization association rules principal component analysis

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mining bilingual data from the web with adaptively learnt patterns 09

Mining bilingual data from the web with adaptively learnt pa...

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Proceedings of the Joint conference of the 47th Annual Meeting of the ACL and the 4th international Joint conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2

作者： Long Jiang Shiquan Yang Ming Zhou Xiaohua Liu Qingsheng Zhu Microsoft Research Asia Beijing P.R. China Chongqing University Chongqing P.R. China

ISBN: (纸本)9781932432466

mining bilingual data (including bilingual sentences and terms) from the Web can benefit many NLP applications, such as machine translation and cross language information retrieval. In this paper, based on the observation that bilingual data in many web pages appear collectively following similar patterns, an adaptive pattern-based bilingual data mining method is proposed. Specifically, given a web page, the method contains four steps: 1) preprocessing: parse the web page into a DOM tree and segment the inner text of each node into snippets; 2) seed mining: identify potential translation pairs (seeds) using a word based alignment model which takes both translation and transliteration into consideration; 3) pattern learning: learn generalized patterns with the identified seeds; 4) pattern based mining: extract all bilingual data in the page using the learned patterns. Our experiments on Chinese web pages produced more than 7.5 million pairs of bilingual sentences and more than 5 million pairs of bilingual terms, both with over 80% accuracy.

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Assessment of contamination condition of insulator based on PSO-SVM

Assessment of contamination condition of insulator based on ...

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IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Shangbin Jiao Ding Liu Guo Xie Yi Deng Department of Automation of Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781424427994

Contamination grades assessment is the important content for the online monitoring system of insulator leakage current (LC). the difficult of assessment is the nonlinear relationship between the electric character variables of the LC, the environment factors and the contamination condition of insulator surface. In this paper, based on laboratory simulation experiments and field data, the parameters of support vector machine (SVM) is optimized by using particle swarm optimization (PSO) arithmetic, then the SVM pattern recognition model of assessment of the contamination grades is constructed. the method takes advantages of the minimum structure risk of SVM and the quickly globally optimizing ability of particle swarm, and the mapping relation between the root mean square (R.M.S.) of LC, the peak value of the LC, the amplitude and times of the pulses of the LC, temperature and humidity of environment and contamination grades may be setup quickly by learning from sample data. Experiment results show that the contamination condition assessment method is effective. then the insulator contamination condition online detection system is developed based on the assessment model.

关键词： Support vector machines Particle swarm optimization Leakage current Dielectrics and electrical insulation Surface contamination Laboratories Arithmetic pattern recognition Optimization methods Root mean square

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machine learning and data mining in pattern recognition - 5th international conference, MLDM 2007, Proceedings

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5th international conference on machine learning and data mining in pattern recognition, MLDM 2007

ISBN: (纸本)9783540734987

the proceedings contain 67 papers. the topics discussed include: on concentration of discrete distributions with applications to supervised learning of classifiers;multi-source data modelling: integrating related data to improve model performance;an empirical comparison of ideal and empirical ROC-based reject rules;outlier detection with kernel density functions;generic probability density function reconstruction for randomization in privacy-preserving data mining;an incremental fuzzy decision tree classification method for mining data streams;on the combination of locally optimal pairwise classifiers;an agent-based approach to the multiple-objective selection of reference vectors;on applying dimension reduction for multi-labeled problems;nonlinear feature selection by relevance feature vector machine;a bounded index for cluster validity;and varying density spatial clustering based on a hierarchical tree.

关键词： data mining

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Analysis of Binary Feature Mapping Rules for Promoter recognition in Imbalanced DNA Sequence datasets using Support Vector machine

Analysis of Binary Feature Mapping Rules for Promoter Recogn...

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4th international IEEE conference Intelligent Systems

作者： Damasevicius, Robertas Kaunas Univ Technol Software Engn Dept LT-51368 Kaunas Lithuania

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.

关键词： DNA mapping rules Support Vector machine data mining promoter recognition bioinformatics

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Comparison of two modern pattern recognition methods

Comparison of two modern pattern recognition methods

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4th international conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Shi Xiaochun Dalian Jiaotong Univ Dept Machine Engn Dalian Liaoning Prov Peoples R China

ISBN: (纸本)9780769532783

Two methods of pattern recognition are introduced in this paper: Unsupervised learning algorithm-fuzzy clustering method and supervised learning algorithm -neural network. the pattern recognition becomes failure pattern recognition if it is used in the fault diagnosis of the machine. Both merits and shortages of these two methods are discussed through a specific example in the mechanical faults diagnosis.

关键词： pattern recognition fuzzy clustering method neural network

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Research on Intrusion Detection System based on pattern recognition

Research on Intrusion Detection System based on pattern reco...

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4th international conference on Networked Computing and Advanced Information Management

作者： Zhu, Youchan Zheng, Ying N China Elect Power Univ Chengdu Peoples R China

ISBN: (纸本)9780769533223

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. this is a typical problem of the classfication,so intrusion detection(ID) can be seen as a pattern recognition problem. In this paper, In this paper, we build the intrusion detection system using Adaboost, a prevailing machine learning algorithm, construction detection classification. In the algorithm, decision RBF neural network are used as weak classifiers. For the training sets is multi-attribute non-linear and massive, we use pattern recognition method of non-linear datadimension reduction algorithm-Isomap algorithm to feature extraction and to improve the speed and training for the handling of classified speed In the feature extraction after the feature of the dimension and Adaboost algorithm training rounds, were studied and experimented. Finally, the experiment proved that Isomap and Adaboost combination of testing the effectiveness of the mothod.

关键词： Intrusion Detection (ID) Adaboost Isomap RBF

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mining Protein Primary Structure data Using Committee machines Approach to Predict Protein Contact Map

Mining Protein Primary Structure Data Using Committee Machin...

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4th international conference on Emerging Technologies (ICET)

作者： Habibi, Narjes K. Mahdaviani, Kaveh Saraee, Mohammad H. Isfahan Univ Technol Dept Elect & Comp Engn Adv Database Syst Data Min & Bioinformat Res Lab Esfahan *** Iran

ISBN: (纸本)9781424422104

Committee machines approach has shown to be useful in different applications. Protein primary structure data contain valuable information to extract. In this paper we mine these data and predict protein contact map based on committee machines. Contact map is the simplified, two dimensional representation of protein spatial structure. Contact map prediction is of great interest due to its application in fold recognition and predicting protein tertiary structure. the results show that the performance of the committee is considerably better than a single model.

关键词： artificial intelligence machine learning committee machine ensemble learning neural network bioinformatics protein contact map contact map prediction

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