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Support vector machine based on a new reduced samples method

Support vector machine based on a new reduced samples method

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Shu-Xia Lu Jie Meng Gui-En Cao Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

The support vectors play an important role in the training to find the optimal hyper-plane. For the problem of many non-support vectors and a few support vectors in the classification of SVM, a method to reduce the samples that may be not support vectors is proposed in this paper. First, adopt the Support Vector Domain Description to find the smallest sphere containing the most data points, and then remove the objects outside the sphere. Second, remove the edge points based on the distance of each pattern to the centers of other classes. In comparison with the standard SVM, the experimental results show that the new algorithm in the paper is capable of reducing the number of samples as well as the training time while maintaining high accuracy.

关键词： Training Accuracy Classification algorithms Kernel Support vector machine classification machine learning

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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 (ICMLC)

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

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.

关键词： Electronic mail Postal services Classification algorithms machine learning Classification tree analysis Association rules

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An Improved Analysis Approach of Overbrace/Underbrace Structure in Printed Mathematical Expressions

An Improved Analysis Approach of Overbrace/Underbrace Struct...

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Asia-Pacific Conf on (CICC-ITOE) Innovative Computing and Communication, Intl Conf on and Information Technology and Ocean Engineering

作者： Zhuo-lun Li Xue-dong Tian Key Laboratory in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

In printed mathematical expression recognition, structural analysis is a key step which determines the overall recognition results. As a special structure in expressions, overbrace/underbrace structure becomes a major difficulty of structural analysis as they're difficult to be distinguished from other structures. On the basis of baseline analysis method of expression structures, this paper presents an improved approach of overbrace/underbrace structure combining region location with variable thresholds. This approach introduces overrun threshold of overbrace/underbrace symbol, which is combined with other features to establish the analysis rules and optimized by testing on a variety of overbrace/underbrace structures. The experimental results show that the approach is effective in determining and analyzing overbrace/underbrace structure.

关键词： Information analysis Underwater communication Information technology Oceans machine learning Computational intelligence Educational institutions Mathematics computer science Testing

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A survey on active learning strategy

A survey on active learning strategy

引用

International Conference on machine learning and Cybernetics (ICMLC)

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

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.

关键词： machine learning Training Classification algorithms Uncertainty learning systems Complexity theory Support vector machines

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PSO-based method for learning similarity measure of nominal features

PSO-based method for learning similarity measure of nominal ...

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Yan Li Xiu-Li Zhang Key Laboratory In Machine Learning and Computational Intelligence Faculty of Mathematics and Computer Science Hebei University Baoding Hebei China

This paper presents a PSO-based method for learning similarity measure of nominal features for case based reasoning classifiers (i.e. CBR classifiers). The symbolic features considered here takes completely unordered values. It has been indicated in that in specific classification task, the similarities between these nominal feature values can not be simply considered as either 0 or 1. A GA-based approach has been developed for learning similarity measure of such feature values. However, when the number of features and feature values become larger, the GA-based algorithm's convergence speed obviously slows down, and the accuracy of classification may be also affected. To address this problem, we propose a PSO-based algorithm for learning similarity measure of nominal features and further describe feature importance through the learned similarity measure. The experimental results show that, using the proposed PSO-based algorithm, the convergence speed is much faster than that of GA-based algorithm and the accuracy is also improved. In addition, we also explain that the feature importance defined through the learned similarities is essentially consistent with that in rough sets, and an illustrative example is finally provided.

关键词： Classification algorithms Accuracy Particle measurements Atmospheric measurements Image color analysis machine learning Convergence

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Data Extraction Based on Index Path in Web

Data Extraction Based on Index Path in Web

引用

International Workshop on Education Technology and computer Science, ETCS

作者： Ya Gao Fang Yuan Ming Zhang Key Laboratory in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9781424463886;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.

关键词： Data mining Web pages HTML Internet Databases computer science XML Web search Search engines Web page design

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An Improved Method for Segmentation of Touching Symbols in Printed Mathematical Expressions

An Improved Method for Segmentation of Touching Symbols in P...

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The 2nd IEEE International Conference on Advanced computer Control(第二届先进计算机控制国际会议 ICACC 2010)

作者： Dong-Yu Zhang Xue-Dong Tian Xin-Fu Li College of Mathematics and Computer Science Hebei Key Laboratory of Machine Learning and Computational Intelligence Hehei University Baoding China

ISBN: (纸本)9781424458455;9781424458486;9781424458479;9781424458462

The segmentation of touching symbols is one of the key factors which affect the performance of printed mathematical expression recognition system. An Improved method for segmentation of touching symbols in printed mathematical expressions is presented. This method is suitable for different types of touching symbols. Firstly, the outer contour of the symbol image is extracted based on contour tracing algorithm. Next, the concave corner points are detected by corner detection algorithm. These concave corner points are considered as the candidate segmentation points. Finally segmentation paths are constructed to achieve the segmentation of the touching symbol.

关键词： OCR Mathematical expression recognition Symbol segmentation Touching symbol segmentation

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Selection of Deep Web Database Based on Retrieval Performance

Selection of Deep Web Database Based on Retrieval Performanc...

引用

International Workshop on Education Technology and computer Science, ETCS

作者： Weijing Li Fang Yuan Ming Zhang Key Laboratory in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9781424463886;9780769539874

A mass of high-quality information included in Deep Web can be accessed, which is still growing rapidly with the rapid development of the World Wide Web. Therefore it becomes more and more important to find the Web databases which are most relevant to the user queries. In this paper we propose a selection method of Web database based on retrieval performance. This method can fix the topic based on website characteristics and then classify the websites. Finally it decides which Web database can be chosen based on the retrieval performance. This method can not only accurately select the Web databases which satisfy the user queries but also improve the speed of the database query and the quality of retrieval.

关键词： Databases Information retrieval Books computer science Costs Content based retrieval Engines computer science education Educational technology machine learning

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Reductions in inconsistent decision systems based on dominance relations

Reductions in inconsistent decision systems based on dominan...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Yan Li Na-Xin Sun Jin Zhao Key Laboratory In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

By incorporating domination principle in inconsistent decision systems based on dominance relations, we define the concept of distribution function for a decision system to directly reflect the inconsistent degree of this system. A new type of distribution reduction and maximum distribution reduction are correspondingly introduced. The relationships between these two reductions are discussed, and their judgment theorems are given. In addition, this article also discusses the relations of several existing reductions, including compatible reduction, absolute reduction, and the proposed distribution reduction and maximum distribution reduction.

关键词： Information systems Distribution functions Approximation methods machine learning Cybernetics Set theory

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A HSC-based sample selection method for Support Vector machine

A HSC-based sample selection method for Support Vector Machi...

引用

International Conference on machine learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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