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检索条件"机构=Hebei Key Laboratory of Machine Learning and Computational Intelligence"

共 208 条记录，以下是21-30 订阅

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Modified AdaBoost based OCSVM ensemble for image retrieval

Modified AdaBoost based OCSVM ensemble for image retrieval

引用

2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： Xing, Hong-Jie Wu, Jian-Guo Chen, Xue-Fang Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781467314855

For the traditional content-based image retrieval system, the number of irrelevant images for a given query image is significantly more than that of relevant images in an image repository. Therefore, the numbers of negative samples and positive samples are highly unbalanced, which makes the traditional binary classifiers ineffective. In this paper, our proposed modified AdaBoost-based one-class support vector machine (OCSVM) ensemble is utilized to deal with the aforesaid problem. In our proposed method, the weight update formula of training data for AdaBoost is modified to make AdaBoost fit for combining the results of OCSVMs even though OCSVM is regarded as a strong classifier. Compared with the other three related methods, our proposed approach exhibits better performance on the three benchmark image databases. © 2012 IEEE.

关键词： Adaptive boosting

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Selective ensemble of support vector data descriptions for novelty detection

Selective ensemble of support vector data descriptions for n...

引用

9th International Symposium on Neural Networks, ISNN 2012

作者： Xing, Hong-Jie Chen, Xue-Fang Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9783642313455

Since support vector data description (SVDD) is regarded as a strong classifier, the traditional ensemble methods are not fit for directly combining the results of several SVDDs. Moreover, as is well-known, when many trained classifiers are available, it is better to ensemble some of them rather than all. In this paper, a selective ensemble method based on correntropy is proposed to deal with the foresaid problems. The base classifier used in the proposed ensemble is SVDD. Experimental results on two synthetic data sets and five benchmark data sets demonstrate that the proposed method is superior to its related approaches. © 2012 Springer-Verlag.

关键词： Data description

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Robust smooth one-class support vector machine 2

Robust smooth one-class support vector machine

引用

2nd International Conference on Information Technology and Electronic Commerce, ICITEC 2014

作者： Hu, Jin-Kou Xing, Hong-Jie Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei Province071002 China

ISBN: (纸本)9781479952984

In this paper, a novel one-class classification approach, namely, robust smooth one-class support vector machine (RSOCSVM) is proposed. The proposed method can efficiently enhance the anti-noise ability of the traditional one-class support vector machine (OCSVM). Utilizing the smooth technique, RSOCSVM reformulates the quadratic programming problem of OCSVM as an unstrained optimization format. Moreover, half-quadratic minimization is used to solve the obtained unstrained optimization problem. Experimental results on two synthetic data sets and nine benchmark data sets demonstrate that the proposed method is superior to the traditional OCSVM. © 2014 IEEE.

关键词： Support vector machines

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An improved approach to ordinal classification 13

An improved approach to ordinal classification

引用

13th International Conference on machine learning and Cybernetics, ICMLC 2014

作者： Wang, Donghui Zhai, Junhai Zhu, Hong Wang, Xizhao Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071002 China

ISBN: (纸本)9783662456514

A simple ordinal classification approach (SOCA) has been proposed by Frank and Hall. SOCA is a general method, any classification algorithm such as C4.5, k nearest neighbors (KNN) algorithm and extreme learning machine (ELM) etc. can be applied to this approach. We find that in SOCA only ordering information of decision attribute is used to classify objects but the ordering information of conditional attributes is not considered. Furthermore we experimentally find that ordering information of conditional attributes can also improve the generalization ability of the classification method. In this paper, we propose an improved ordinal classification methodology by employing the ordering information of both condition and decision attributes. In addition, we analyze the sensitivity of the SOCA on performance to the underlying classification algorithms, for instance, C4.5, KNN and ELM. A number of experiments are conducted and the experimental results show that the proposed method is feasible and effective. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Decision trees

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Linear discriminant analysis based on Zp-norm maximization 2

Linear discriminant analysis based on Zp-norm maximization

引用

2nd International Conference on Information Technology and Electronic Commerce, ICITEC 2014

作者： An, Lei-Lei Xing, Hong-Jie Key Laboratory of Machine Learning and Computational Intelligence College of Computer Science and Technology Hebei University Baoding Hebei Province071002 China

ISBN: (纸本)9781479952984

In this paper, linear discriminant analysis (LDA) based on Lp-norm (LDA-Lp) optimization method is proposed. The objective function utilizing the Lp-norm with arbitrary p value is studied. By maximizing the Lp-norm-based ratio between the between-class scatter and the within-class scatter, LDA-Lp can construct a set of local optimal projection vectors. Moreover, the optimal projection vectors can be obtained by the gradient ascent method. Experimental results on the two synthetic and fourteen benchmark datasets demonstrate that the better performance of LDA-Lp can be achieved by choosing the optimal value of p. © 2014 IEEE.

关键词： Benchmarking

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Imbalanced extreme support vector machine

Imbalanced extreme support vector machine

引用

2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： Zhou, Xu Lu, Shu-Xia Hu, Li-Sha Zhang, Meng Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781467314855

For the problem of imbalanced data classification which was not discussed in the standard Extreme Support Vector machines (ESVM), an imbalanced extreme support vector machines (IESVM) was proposed. Firstly, a preliminary normal vector of separating hyperplane is obtained directly by geometric analysis. Secondly, penalty factors are obtained which are based on the information provided by data sets projecting onto the preliminary normal vector. Finally, the final separation hyperplane is got through the improved ESVM training. IESVM can overcome disadvantages of traditional designing methods which only consider the imbalance of samples size and can improve the generalization ability of ESVM. Experimental results show that the method can effectively enhance the classification performance on imbalanced data sets. © 2012 IEEE.

关键词： Support vector machines

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Monotonic decision tree for interval valued data 13

Monotonic decision tree for interval valued data

引用

13th International Conference on machine learning and Cybernetics, ICMLC 2014

作者： Zhu, Hong Zhai, Junhai Wang, Shanshan Wang, Xizhao Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding071002 China

ISBN: (纸本)9783662456514

Traditional decision tree algorithms for interval valued data only can deal with non-ordinal classification problems. In this paper, we presented an algorithm to solve the ordinal classification problems, where both the condition attributes with interval values and the decision attributes meet the monotonic requirement. The algorithm uses the rank mutual information to select extended attributes, which guarantees that the outputted decision tree is monotonic. The proposed algorithm is illustrated by a numerical example, and a monotonically consistent decision tree is generated. The design of algorithm can provide some useful guidelines for extending real-vauled to interval-valued attributes in ordinal decision tree induction. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Decision trees

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An image retrieval method about ancient Chinese characters within local area

An image retrieval method about ancient Chinese characters w...

引用

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

作者： Tian, Xue-Dong Huang, Juan Yang, Fang Ha, Yan College of Mathematics and Computer Science Hebei University Baoding Hebei China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Hebei China

ISBN: (纸本)9781457717017

Ancient Chinese characters normally have complex structures which are composed of many strokes. Different characters may show a similar shape which results in unsatisfactory answers for their image retrieving using the traditional methods. In this paper, an image retrieval algorithm for ancient Chinese characters is proposed based on the local area of characters. First of all, according to the need of users, the regions needed to be matched are marked on the sample of ancient Chinese character image. Then we match the image by comparing the Euclidean distance of feature parameters between sample image and database images. Finally, the ancient images which users need are found. The experimental results show that the proposed method could improve the efficiency of correlative research. © 2011 IEEE.

关键词： Image retrieval

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Image classification by combining local and mid-level features 18

Image classification by combining local and mid-level featur...

引用

2nd International Conference on Innovation in Artificial intelligence, ICIAI 2018

作者： Lu, Yao Zhang, Hui Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Hebei China

ISBN: (纸本)9781450363457

It is meaningful to study high performance image classification algorithms for massive image management and effective organization. Image feature representations directly affect the performance of classification algorithm, however, in the current popular sparse coding framework for image classification, the relationship between local features is neglected, and there is still considerable room left for further improvement. In this paper, mid-level features are extracted efficiently in the superpixel region, which embeds the structural information of local features. Then we combine the local features and mid-level features to achieve better discriminative ability under the sparse coding framework. Compared to using only local features, the new methods improve the classification performance about 3% on several popular benchmarks. © 2018 Association for Computing machinery.

关键词： Image classification

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Selection of deep web database based on retrieval performance

Selection of deep web database based on retrieval performanc...

引用

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

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

ISBN: (纸本)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. © 2010 IEEE.

关键词： Websites

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