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learning the parameters for least squares support vector machine

Learning the parameters for least squares support vector mac...

引用

2011 7th International Conference on Natural Computation, ICNC 2011

作者： Lu, Shuxia Fan, Xiaoxue Hu, Lisha Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9781424499533

The regularization parameter and kernel parameter play important roles in the performance of the least squares support vector machine (LS-SVM). Aimed at optimizing the LS-SVM's parameters, a fast method based on distance is presented. The method is by way of calculating the various types of distances in the feature space to determine the optimal kernel parameter. Since the method only needs to calculate some simple mathematical formulas, and avoids training the corresponding LS-SVM classifiers, the method can greatly reduce the training time. Experiment results show that the proposed method can improve the training speed. © 2011 IEEE.

关键词： Support vector machines

来源：评论

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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

来源：评论

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A method for person name disambiguation based on Baidu Encyclopedia

A method for person name disambiguation based on Baidu Encyc...

引用

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

作者： Li, Xinfu Cao, Wenxue Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9781457717017

The phenomenon of person name ambiguity is widespread on web pages in that one name may be used by different people. It is important to uniquely identify the given person on the web. In this paper, the method Baidu-PND is proposed by the authors. It is an unsupervised name disambiguation method based on Baidu Encyclopedia. We extract three features including background knowledge, contextual feature and Related-Set of the characters from the online Baidu Encyclopedia. The weights of the features are studied by logistic regression algorithm. Then we make a linear fusion of the features. The maximum combined value is selected as the correct person on web pages. Experiments are conducted to measure the performance of Baidu-PND, which show that the performance is higher than we expected, validating its feasibility and effectiveness for person name disambiguation on web pages. And, Baidu-PND is a new method for knowledge mining based on Baidu Encyclopedia. © 2011 IEEE.

关键词： Websites

来源：评论

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Instances selection for NN with fuzzy rough technique

Instances selection for NN with fuzzy rough technique

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Kang, Xiao-Meng Liu, Xiao-Peng Zhai, Jun-Hai Zhai, Meng-Yao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The NN algorithm is a simple and well-known supervised learning scheme which classifies an unseen instance by finding its closest neighbor in training set. The main drawback of NN is that the whole training set must be stored in the computer to classify an unseen instance. In order to deal with this problem, P. Hart proposed the condensed nearest neighbor (CNN) algorithm. However, CNN select the important instances from the whole training set, which suffers from the problem of large memory requirement same as NN. In this paper, we propose an algorithm to select instances from the border region with fuzzy rough technique. The experimental results demonstrate the effectiveness of our proposed method. © 2011 IEEE.

关键词： Classification (of information)

来源：评论

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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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Potential support vector machine based on the reduced samples

Potential support vector machine based on the reduced sample...

引用

International Conference on Information Science and Engineering

作者： Lu, Shu-Xia Cao, Gui-En Meng, Jie Wang, Hua-Chao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

ISBN: (纸本)9781424480968

When the training dataset is very large, the learning process of potential support vector machine takes up so large memory that the training speed is very slow. To accelerate the training speed of the potential support vector machine (PSVM) for large-scale datasets, a new method is proposed, which introduces PSVM based on the reduced samples. The new method removes most non-support vectors, and keeps the samples on and near the boundary, which may be the support vectors, as the new training samples. This method is more suitable to large-scale datasets. The experimental results show that the proposed method performs well to decrease the consumption of computer memory,and accelerate the training speed of PSVM. © 2010 IEEE.

关键词： Support vector machines

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The condensed fuzzy k-nearest neighbor rule based on sample fuzzy entropy

The condensed fuzzy k-nearest neighbor rule based on sample ...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Zhai, Jun-Hai Li, Na Zhai, Meng-Yao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The fuzzy k-nearest neighbor (F-KNN) algorithm was originally developed by Keller in 1985, which generalized the k-nearest neighbor (KNN) algorithm and could overcome the drawback of KNN in which all of instances were considered equally important. However, the F-KNN algorithm still suffers from the problem of large memory requirement same as the KNN. In order to deal with the problem, this paper proposes the condensed fuzzy k-nearest neighbor rule (CFKNN) which selects the important instances based on sample fuzzy entropy. The experimental results show that our proposed method is feasible and effective. © 2011 IEEE.

关键词： Motion compensation

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An improved cluster oriented fuzzy decision trees 1

引用

12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009

作者： Su, Shan Wang, Xizhao Zhai, Junhai Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (数字)9783642106460

ISBN: (纸本)3642106455

In this paper, an improved cluster oriented decision trees algorithm shortly named ICFDT is presented. In this algorithm, fuzzy C-means clustering algorithm (FCM) without instance lab.ls is used to split the nodes and two novel node expanding criteria are proposed. One criterion uses the ratio of homogenous samples in the node to split;the other splits the node by membership degree without lab.ls. The experimental results in artificial and machine learning datasets show that our method can achieve better performance comparing to standard decision tree named C4.5. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Decision trees

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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

作者： Sun, Li-Li Wang, Xi-Zhao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781424465262

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. © 2010 IEEE.

关键词： Surveys

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KM-A* pathfinding algorithm based on hierarchical clustering and strengthened DB Index criteria

KM-A* pathfinding algorithm based on hierarchical clustering...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Chen, Cai Li, Yan Li, Tie-Song Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

In computer games, high-quality pathfinding algorithms are important to bring satisfactory experiences to the players, which may improve the playability of computer game. The method of KM-A belongs to hierarchical pathfinding, which incorporates the information of obstacle distribution when partitioning game maps using K-means clustering. This algorithm reduced unnecessary storage and unnatural paths in some open areas. However, the found path by KM-A is very unstable and the quality closely depends on the clustering results. In this paper, we proposed an improved KM-A algorithm, which aims to enhance the stability and quality of pathfinding. First, an agglomerative hierarchical clustering algorithm is used to determine the initial cluster centers, and K-Means algorithm is then applied to cluster obstacle nodes with k values which can possibly generate best cluster categories on game maps. Secondly, the strengthened form of DB Index criteria is defined by adding impact factors to the standard DB Index criteria to measure the quality of clustering results. Finally the best clustering is selected based on this new DB Index, which will provide a guidance to further generate an abstract map for on-line pathfinding. Experiments are conducted to compare the performance of KM-A and improved KM-A, which show that the latter can find more stable paths and use less on-line processing time. © 2011 IEEE.

关键词： Computer games

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