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Ensemble online sequential extreme learning machine for large data set classification

Ensemble online sequential extreme learning machine for larg...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Junhai ZHAI Jinggeng WANG Xizhao WANG College of Mathematics and Computer Science Hebei University Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Computer Science Hebei University

ISBN: (纸本)9781479938414

Online sequential extreme learning machine (OS-ELM) proposed by Liang et al. employ sequential learning strategy to learn the target concept from the data. Compared with the original ELM, OS-ELM can learn data one-by-one or chunk-by-chunk with fixed or varying chunk size with almost same performance as ELM. While compared with other state-of-the-art sequential algorithms such as SGBP, RAN and GAP-RBF, OS-ELM has faster learning speed and better generalization ability. However, similar to ELM, OS-ELM also has instability in different trials of simulations. In addition, for large data sets, OS-ELM will not halt when there are training samples not be learned, this phenomenon results in long learning time. In order to deal with the problems, this paper proposes an algorithm named E-OS-ELM for integrating OS-ELM to classify large data sets. The experimental results show that the proposed method is effective and efficient;it can effectively overcome the drawbacks mentioned above.

关键词： Extreme learning machine Ensemble Sequential learning Large data set

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A mathematical formula retrieval method using structure sub-tree

A mathematical formula retrieval method using structure sub-...

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3rd International Conference on Materials Science and Information Technology, MSIT 2013

作者： Guan, Ming Jie Tian, Xue Dong Yang, Fang Yang, Song Qiang College of Mathematics and Computer Science Hebei University Baoding China Hebei key laboratory of Machine Learning and Computational Intelligence Hebei University China

ISBN: (纸本)9783037857700

It is quite inadequate in providing formula retrieval function by traditional retrieval techniques used in full-text information retrieval system. The main reason is that there are many difficulties to extract the keywords of the mathematical formulas. In this paper, a detailed analysis of the structural characteristics of mathematical formulas and existing index mechanism of mathematical formula searching engine is fulfilled. Then a full-text index (named SLIndex) of mathematical formulas with B+ tree structure is designed and implemented which extracts the structured logic sub-tree feature as keywords of formulas and employs inverted index. Finally, a formula search engine model based on SLIndex is implemented in Apache 2.0 web server. © (2013) Trans Tech Publications, Switzerland.

关键词： Search engines

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Audio retrieval based on Chinese keyword search in relational databases

Audio retrieval based on Chinese keyword search in relationa...

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3rd International Conference on Materials Science and Information Technology, MSIT 2013

作者： Zhu, Bo Yan Liu, Guang Zhu, Liang College of Art Hebei University Baoding Hebei 071002 China Key Laboratory of Machine Learning and Computational Intelligence School of Mathematics and Computer Science Hebei University Baoding Hebei 071002 China

ISBN: (纸本)9783037857700

In this paper, we propose a new method based on Chinese keyword search to select the WAV or MP3 files in audio post-production. First, we listen to each file and label it with Chinese characters, and then classify and store the files in a relational database system. Then, we use the techniques of Chinese keyword search to match query characters and the tuple characters quickly, and to compute similarities between the query and candidate tuples. For the characteristics of Chinese keyword search, we present a ranking strategy and an algorithm to refine the candidate tuples resulting from the first round matching, and finally get top-N results of audio files. The experimental results show that our method is efficient and effective. © (2013) Trans Tech Publications, Switzerland.

关键词： Relational database systems

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An indexing method of mathematical expression retrieval

An indexing method of mathematical expression retrieval

引用

International Conference on Computer Science and Network Technology (ICCSNT)

作者： Xuedong Tian Songqiang Yang Xinfu Li Fang Yang Hebei key laboratory of Machine Learning and Computational Intelligence Baoding China College of Mathematics & Computer Science Hebei University Baoding China

ISBN: (纸本)9781479905607

As the kernel component of scientific documents, mathematical expressions are becoming a new object of searching engines. Different from normal text, mathematical expressions are composed of various kinds of symbols arranged in nonlinear mode, which results in the limitations of traditional full-text information retrieval used for expression searching. In this paper, we discuss the existing search engine of mathematical expressions and introduce the two-dimensional characteristics of mathematical expressions firstly. Then, a data structure of expressing mathematical formulas is designed which contains not only the symbol code but also the mathematical information among symbols. Finally, the indexing algorithm of mathematical expressions is put forward on the basis of the expression data structure. The experimental result shows the effectiveness of the indexing method proposed in this paper.

关键词： Feature extraction Search engines Data mining Indexing Educational institutions Data structures

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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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Modified AdaBoost based OCSVM ensemble for image retrieval

Modified AdaBoost based OCSVM ensemble for image retrieval

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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 feature extraction for novelty detection based on regularized correntropy criterion

Robust feature extraction for novelty detection based on reg...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Huan-Ru Ren Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

ISBN: (纸本)9781467317139

In this paper, a robust feature extraction method based on regularized correntropy criterion (RCC) is proposed for novelty detection. In RCC, the criterion aims to maximize the difference between the correntropy of the normal data with their mean and the correntropy of the novel data with the mean of normal data. Moreover, the optimal projection vectors in the proposed objective function can be obtained by the half-quadratic (HQ) optimization technique with an iterative manner. Experimental results on one synthetic data set and nine benchmark data sets for novelty detection demonstrate that the proposed method is superior to its related approaches.

关键词： Correntropy Half-quadratic optimization Feature extraction Novelty detection Feature extraction Novelty Objective function Remote Control Center Renal Cell Carcinoma Dataset

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce 1

引用

7th IFIP International Conference on Intelligent Information Processing, IIP 2012

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

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

关键词： MapReduce

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Modified AdaBoost based OCSVM ensemble for image retrieval

Modified AdaBoost based OCSVM ensemble for image retrieval

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International Conference on machine learning and Cybernetics (ICMLC)

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

关键词： Image retrieval Abstracts Image color analysis

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