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共 133 条记录，以下是21-30 订阅

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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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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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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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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

作者： Li, Yan Zhang, Xiu-Li Key Lab. in Machine Learning and Computational Intelligence Faculty of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781424465262

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 [3] 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. © 2010 IEEE.

关键词： Particle swarm optimization (PSO)

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Reductions in inconsistent decision systems based on dominance relations

Reductions in inconsistent decision systems based on dominan...

引用

International Conference on machine learning and Cybernetics

作者： Li, Yan Sun, Na-Xin Zhao, Jin Key Lab. In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China

ISBN: (纸本)9781424465262

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. © 2010 IEEE.

关键词： Distribution functions

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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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Research on location and similar comments in point-of-interest recommendation system for users

Research on location and similar comments in point-of-intere...

引用

2020 International Conference on Data Processing Algorithms and Models, ICDPAM 2020

作者： Liu, Yu Wei, Ning College of Mathematics and Information Science Hebei University Baoding Hebei071002 China Hebei Key Laboratory of Machine Learning and Computational Intelligence China

By combining deep learning algorithms and potential probabilistic topic models, this paper proposes a new modeling strategy for individual users and constructs an effective point of interest recommendation system. First, Doc2vec is used to vectorize the user's comments, and the Bow model is used to match the similarity of the points of interest, and then the potential probability model is used to cluster the positions of the points of interest. Finally, relying on the user's historical comment data as a basis, the Top-K ranking recommendation scheme is obtained. The experimental results show that for large-scale location-based social network data sets, the algorithm in this paper has an ideal final effect on users' personalized recommendations. The recommendation model for user location information and similar comment points of interest proposed in this paper is more subjectively and objectively in line with user behavior needs. On the premise of ensuring the real-time performance of recommendation, the model proposed in this paper effectively improves the accuracy of recommendation compared with traditional point-of-interest recommendation algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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A mathematical formula retrieval method using structure sub-tree

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

引用

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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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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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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