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2nd International Workshop on Education Technology and Computer Science, ETCS 2010

作者： Gao, Ya Yuan, Fang Zhang, Ming Key Lab. in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University BaodingHebei China

ISBN: (纸本)9780769539874

Data extraction in Web is to obtain the desired information to users in Web pages. For a more accurately valuable data extraction, this paper proposes a new method called data extraction based on index path in Web (DEIP). This approach establishes the index path for each text node using XML DOM;defines the prefix of data-rich by keywords in the index path;generate extraction rule and obtain a wrapper according. The wrapper can extract data automatically in the same domain from a Website. It does relevant to the continuity, the structural similarity, and the location relations of the useful information in Web pages, but not the HTML tag, Experiments indicate that this method is efficient in the recall and the precision of data extraction. © 2010 IEEE.

关键词： XML

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learning the parameters for least squares support vector machine

Learning the parameters for least squares support vector mac...

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2011 7th International Conference on Natural Computation, ICNC 2011

作者： Lu, Shuxia Fan, Xiaoxue Hu, Lisha Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9781424499533

The regularization parameter and kernel parameter play important roles in the performance of the least squares support vector machine (LS-SVM). Aimed at optimizing the LS-SVM's parameters, a fast method based on distance is presented. The method is by way of calculating the various types of distances in the feature space to determine the optimal kernel parameter. Since the method only needs to calculate some simple mathematical formulas, and avoids training the corresponding LS-SVM classifiers, the method can greatly reduce the training time. Experiment results show that the proposed method can improve the training speed. © 2011 IEEE.

关键词： Support vector machines

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A method for person name disambiguation based on Baidu Encyclopedia

A method for person name disambiguation based on Baidu Encyc...

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2011 International Conference on Transportation, Mechanical, and Electrical Engineering, TMEE 2011

作者： Li, Xinfu Cao, Wenxue Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9781457717017

The phenomenon of person name ambiguity is widespread on web pages in that one name may be used by different people. It is important to uniquely identify the given person on the web. In this paper, the method Baidu-PND is proposed by the authors. It is an unsupervised name disambiguation method based on Baidu Encyclopedia. We extract three features including background knowledge, contextual feature and Related-Set of the characters from the online Baidu Encyclopedia. The weights of the features are studied by logistic regression algorithm. Then we make a linear fusion of the features. The maximum combined value is selected as the correct person on web pages. Experiments are conducted to measure the performance of Baidu-PND, which show that the performance is higher than we expected, validating its feasibility and effectiveness for person name disambiguation on web pages. And, Baidu-PND is a new method for knowledge mining based on Baidu Encyclopedia. © 2011 IEEE.

关键词： Websites

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CET4 passing rate analysis based on fuzzy decision tree induction and active learning

CET4 passing rate analysis based on fuzzy decision tree indu...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Qiao, Qing-Shui Wang, Hai-Tao Wang, Zhen-Yu Zhai, Jun-Hai Dept. of English Hebei Institute of Civil Engineering and Architecture Zhangjiakuo City Hebei China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding City China

ISBN: (纸本)9781457703065

College English Test Band Four (CET4) in China has been a significant impact on evaluating the English preliminary level of a college student or a class. How to improve the college English teaching and go further to raise passing rate of CET4 are a challenge for many colleges and universities. This paper makes an attempt to quantitatively analyze the CET4 and exam-related factors by using fussy decision tree technique and active learning based on uncertainty. Several features are selected to formulate this problem. The weighted margin is proposed as the new uncertainty measure criterion for unlab.led instance, and a density measure is introduced for avoiding selecting isolated instances. Experiments and simulations on different classes of students show the proposed quantitative analysis method is feasible and effective, which can provide teachers with some useful guidelines for how to improve the college English teaching. © 2011 IEEE.

关键词： Decision trees

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Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering

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Tsinghua Science and Technology 2022年第5期27卷 855-868页

作者： Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province the College of Mathematics and Information ScienceHebei UniversityBaoding 071002China School of Applied Mathematics Beijing Normal University ZhuhaiZhuhai 519087China Department of Computer Teaching Hebei UniversityBaoding 071002China

The performances of semisupervised clustering for unlab.led data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation *** a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among *** these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering ***,the diversity and amount of input information are enhanced to generate highly useful representations for downstream ***,the topological graph is constructed to express the spatial relationship of *** a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively *** external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering *** results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service *** GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech ***,DBI indicates the dispersion of cluster distribution and compactness within the cluster.

关键词： self-supervised clustering graph convolutional network feature correspondence semantic feature guidance confusion matrix evaluation indicator

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The condensed fuzzy k-nearest neighbor rule based on sample fuzzy entropy

The condensed fuzzy k-nearest neighbor rule based on sample ...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Zhai, Jun-Hai Li, Na Zhai, Meng-Yao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The fuzzy k-nearest neighbor (F-KNN) algorithm was originally developed by Keller in 1985, which generalized the k-nearest neighbor (KNN) algorithm and could overcome the drawback of KNN in which all of instances were considered equally important. However, the F-KNN algorithm still suffers from the problem of large memory requirement same as the KNN. In order to deal with the problem, this paper proposes the condensed fuzzy k-nearest neighbor rule (CFKNN) which selects the important instances based on sample fuzzy entropy. The experimental results show that our proposed method is feasible and effective. © 2011 IEEE.

关键词： Motion compensation

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A survey on active learning strategy

A survey on active learning strategy

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International Conference on machine learning and Cybernetics

作者： Sun, Li-Li Wang, Xi-Zhao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781424465262

Active learning is a hot topic in machine learning field. The main task of active learning is to automatically select the representative instances for efficiently reducing the sample complexity. This paper presents a brief survey of active learning regarding selection methods, query strategies, applications and other related works. © 2010 IEEE.

关键词： Surveys

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An improved cluster oriented fuzzy decision trees 1

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12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009

作者： Su, Shan Wang, Xizhao Zhai, Junhai Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (数字)9783642106460

ISBN: (纸本)3642106455

In this paper, an improved cluster oriented decision trees algorithm shortly named ICFDT is presented. In this algorithm, fuzzy C-means clustering algorithm (FCM) without instance lab.ls is used to split the nodes and two novel node expanding criteria are proposed. One criterion uses the ratio of homogenous samples in the node to split;the other splits the node by membership degree without lab.ls. The experimental results in artificial and machine learning datasets show that our method can achieve better performance comparing to standard decision tree named C4.5. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Decision trees

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Instances selection for NN with fuzzy rough technique

Instances selection for NN with fuzzy rough technique

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Kang, Xiao-Meng Liu, Xiao-Peng Zhai, Jun-Hai Zhai, Meng-Yao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The NN algorithm is a simple and well-known supervised learning scheme which classifies an unseen instance by finding its closest neighbor in training set. The main drawback of NN is that the whole training set must be stored in the computer to classify an unseen instance. In order to deal with this problem, P. Hart proposed the condensed nearest neighbor (CNN) algorithm. However, CNN select the important instances from the whole training set, which suffers from the problem of large memory requirement same as NN. In this paper, we propose an algorithm to select instances from the border region with fuzzy rough technique. The experimental results demonstrate the effectiveness of our proposed method. © 2011 IEEE.

关键词： Classification (of information)

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The application of decision tree in Chinese email classification

The application of decision tree in Chinese email classifica...

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International Conference on machine learning and Cybernetics

作者： Chen, Hao Zhan, Yan Li, Yan Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781424465262

Email is a kind of semi-structured document, some important attributes are contained in its structure, and especially using spam-specific features could improve the email classification results. In this paper, we apply decision tree data mining technique to dig out the potential association rules among these attributes of email, and then to identify unknown email's category based on these rules. According to the experiment of applying numerous Chinese emails to our email classifier, the efficiency of our method is not lower than that of other existing methods of checking whole email content text. Meanwhile our method can reduce the cost of computation and consumption of system resources. © 2010 IEEE.

关键词： Association rules

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