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检索条件"机构=Key Lab. of Machine Learning and computational Intelligence"

共 91 条记录，以下是41-50 订阅

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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

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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)

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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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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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Separation theorem and maximal margin classification for fuzzy number spaces

Separation theorem and maximal margin classification for fuz...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： He, Qiang Li, Hong-Liang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The theory of machine learning in metric space is a new research topic and has drawn much attention in recent years. The theoretical foundation of this topic is the question under which conditions two sample sets can be separated in this space. In this paper, motivated by developing a new support vector machine (SVM) in fuzzy number space, we present a necessary and sufficient condition of separating two finite classes of samples by a hyper-plane in n-dimensional fuzzy number space. We also present an attainable expression of maximal margin of the separating hyper-planes which includes some cases of the classes of infinite samples in n-dimensional fuzzy number space. These results generalize and improve the corresponding conclusions for the theory of SVM in Hilbert space to fuzzy number space. © 2011 IEEE.

关键词： Support vector machines

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A survey of the initialization of centers and widths in radial basis function network for classification

A survey of the initialization of centers and widths in radi...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Dong, Chun-Ru Chan, Patrick P.K. Ng, Wing W.Y. Yeung, Daniel S. Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)9781457703065

The radial basis function network (RBFN) has been widely used in various fields such as function regression, pattern recognition, and error detection, etc. However, the structural parameters of RBFN including the number of hidden units, centers vectors, and widths (variances) are one of the most importent issues when training a RBFN, which greatly affect the performance of RBFN. So, the objective of this paper is to construct an elementary survey about this problem. Firstly, the fundamental knowledge and notations of RBFN is introduced. Secondly, we summarize most existing network structure initialization methods for RBFN and categorize them into four goups. Then some typical appraoches for each category are introduced and discussed. The disadvantages and virtues for parts of methods are also introduced. Finally, the paper is concluded with a discussion of current difficulties and possible future directions about RBFN architecture selection. © 2011 IEEE.

关键词： Radial basis function networks

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CET4 passing rate analysis based on fuzzy decision tree induction and active learning

CET4 passing rate analysis based on fuzzy decision tree indu...

引用

2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Qiao, Qing-Shui Wang, Hai-Tao Wang, Zhen-Yu Zhai, Jun-Hai Dept. of English Hebei Institute of Civil Engineering and Architecture Zhangjiakuo City Hebei China Key Lab. in Machine Learning and Computational Intelligence of Hebei Province Baoding City China

ISBN: (纸本)9781457703065

College English Test Band Four (CET4) in China has been a significant impact on evaluating the English preliminary level of a college student or a class. How to improve the college English teaching and go further to raise passing rate of CET4 are a challenge for many colleges and universities. This paper makes an attempt to quantitatively analyze the CET4 and exam-related factors by using fussy decision tree technique and active learning based on uncertainty. Several features are selected to formulate this problem. The weighted margin is proposed as the new uncertainty measure criterion for unlab.led instance, and a density measure is introduced for avoiding selecting isolated instances. Experiments and simulations on different classes of students show the proposed quantitative analysis method is feasible and effective, which can provide teachers with some useful guidelines for how to improve the college English teaching. © 2011 IEEE.

关键词： Decision trees

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Optimization methods for resources allocation in real-time strategy games

Optimization methods for resources allocation in real-time s...

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2011 International Conference on machine learning and Cybernetics, ICMLC 2011

作者： Tong, Xiao-Lei Li, Yan Li, Wen-Liang Zhang, Lei Key Lab. In Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei Province China Handan Hospital of Traditional Chinese Medicine Handan 056000 China

ISBN: (纸本)9781457703065

In order to meet the demands of the real time strategy (RTS) games, two learning methods are proposed based on genetic algorithm (GA) and Particle swarm optimization (PSO) to handle the problem of multi-team weapon target assignment (MT-WTA) and distribution of defensive position with restrictive limit of weapon resource. The goal is to take the greatest destruction on the targets. Firstly, we use GA to assign different types of weapons under limited resources. Secondly, we put the optimal results from the first stage into a random game's map to obtain final defensive locations. Then GA and PSO are used to achieve the best distribution of defensive positions respectively and their performance is compared in RTS Games. Both of these two methods have provided efficient, interesting AI to solve real-time strategy games problems, the experimental results can well support this point. © 2011 IEEE.

关键词： Genetic algorithms

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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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