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

共 131 条记录，以下是81-90 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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Case-based multi-task pathfinding algorithm

Case-based multi-task pathfinding algorithm

引用

2012 International Conference on machine learning and Cybernetics, ICMLC 2012

作者： Li, Yan Su, Lan-Ming He, Qiang Key Lab. in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781467314855

Pathfinding is an important task in computer games, where the algorithm efficiency is the key issue. In this paper, we introduce case-based reasoning method in the process of A* algorithm in multi-task pathfinding. Firstly, we save some typical paths as cases. When a new task is coming, it no longer uses A* to find a path from scratch, but firstly computes the similarity of the new task and the stored cases to decide whether to go along the previous found paths or not. A solution to the new task will be obtained after adapting to the found similar case(s). Obviously, this memory-based pathfinding can reduce the search time at the cost of using more memory to store found paths as cases. Through experimental results, it is demonstrated that, as the number of stored paths is increasing, fewer nodes are needed to be searched during the pathfinding process. © 2012 IEEE.

关键词： Case based reasoning

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Chinese functional chunk identification based on part of speech

Chinese functional chunk identification based on part of spe...

引用

International Conference on Communication Systems and Network Technologies, CSNT 2012

作者： Wang, Cheng-Yan Li, Xin-Fu Tian, Xue-Dong College of Mathematics and Computer Science Hebei University Baoding China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province China

ISBN: (纸本)9780769546926

Chinese functional chunk describes the basic skeleton of the Chinese sentences. It is the important bridge for joining syntax and semantic description, and the Chinese functional chunk identification plays a key role in the field of natural language processing. Identifying the functional chunk is mostly based on methods of rules which come from the syntactic analysis. In this paper, the functional chunk is mainly identified according to the effect of the part of speech in the syntactic analysis. The experimental results show the proposed method works well on the functional chunk identification. © 2012 IEEE.

关键词： Semantics

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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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Robust feature extraction for novelty detection based on regularized correntropy criterion

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

引用

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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Modified AdaBoost based OCSVM ensemble for image retrieval

Modified AdaBoost based OCSVM ensemble for image retrieval

引用

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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Chinese Functional Chunk Identification Based on Part of Speech

Chinese Functional Chunk Identification Based on Part of Spe...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Cheng-Yan Wang Xin-Fu Li Xue-Dong Tian College of Mathematics and Computer Science Hebei University Baoding China College of Mathematics and Computer Scienc Key Laboratory in Machine Learning and Computational Intelligence of Hebei Province Hebei University Baoding China

关键词： Tagging Speech Syntactics Educational institutions Statistical analysis Information processing Particle separators

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Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

引用

13th ACIS International Conference on Software Engineering, Artificial intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

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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: (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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