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Chinese text categorization study based on CBM learning

Chinese text categorization study based on CBM learning

引用

2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Zhan, Yan Chen, Hao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9781424459346

Text Categorization (TC) is an important component in many information organization and information management tasks. In many TC applications, the case-base grows at a fast rate and this causes inefficiency in the case retrieval process. Using Case-Base Maintenance learning via the GC (Generalization Capability) altorithm, which can reduce the case number into KNN algorithm, can improve efficiency when indexing near neighbor in K-Nearest Neighbor algorithm. The numerical experiments prove the validity of this learning algorithm. Since K-NN algorithm is used extensively to a variety of areas, we can improve classification performance further in TC. ©2010 IEEE.

关键词： Information management

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Feature extended short text categorization based on theme ontology

Feature extended short text categorization based on theme on...

引用

2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

ISBN: (纸本)9781467300223

Short text classification problem as text classification a branch, in addition to the same with traditional text classification to a certain degree, still need to face some special problems to be solved, because of short text length, features sparse, measuring the words difficultly. Due to the nature of ontology emphasize related field concept, which has the defect of few characteristics in short text, it is necessary to emphasize the relationship between semantic. This paper uses the feature extended method based on theme Ontology. As considering the semantic relations, it can get better classification performance compared to the conventional method. Meanwhile, using Case-Base Maintenance learning via the GC (Generalization Capability) algorithm, which can reduce the case number into K-NN algorithm, can improve efficiency when indexing near neighbor in K-Nearest Neighbor algorithm. The numerical experiments prove the validity of this learning algorithm. © 2012 IEEE.

关键词： Ontology

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An improved research method about the similarity calculation of ontology

An improved research method about the similarity calculation...

引用

2009 International Conference on Information Engineering and computer Science, ICIECS 2009

作者： Li, Xin-Fu Yin, Peng Zheng, Mei Key Lab. in Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Institute of International Education Yanshan University Qinhuangdao China

ISBN: (纸本)9781424449941

Ontology mapping has been widely used in ontology application, but the similarity calculation becomes a thorny issue in the process of ontology mapping. In this paper, the different elements of ontology are considered. Semantic similarity and concept similarity are integrated to get the ontology similarity. During the similarity calculation of the concept attributes, filter strategy is used to reduce the number of concept attributes, and a method of attributes similarity calculation is proposed. In addition, comprehensive similarity calculation method is given including the semantic and the concept of ontology. The experiment results show that this approach can improve the precision effectively. ©2009 IEEE.

关键词： Ontology

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Markov property of Markov chains and its test

Markov property of Markov chains and its test

引用

International Conference on machine learning and Cybernetics

作者： Zhang, Yu-Fen Zhang, Qun-Feng Yu, Rui-Hua Key Lab. for Machine Learning and Computational Intelligence College of Mathematics and Computer Hebei University Baoding 071002 China Department of Information Technology Baoding University Baoding 071000 China

ISBN: (纸本)9781424465262

Markov chains, with Markov property as its essence, are widely used in the fields such as information theory, automatic control, communication techniques, genetics, computer sciences, economic administration, education administration, and market forecasts. While using Markov chains to predict the future events, we must test the Markov property of random variable sequences of the past statistic data. Only when the random variable sequences satisfy the Markov property, can the prediction could be precise. This paper discusses the concept of Markov property and its features, studies its test method, and by example demonstrates the effectiveness of this prediction method. © 2010 IEEE.

关键词： Random variables

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A comparative study of four Fuzzy Integrals for classifier fusion

A comparative study of four Fuzzy Integrals for classifier f...

引用

International Conference on machine learning and Cybernetics

作者： Feng, Hui-Min Li, Xue-Fei Chen, Jun-Fen Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China College of Science Agricultural University of Hebei Baoding 071001 China

ISBN: (纸本)9781424465262

Fuzzy Integral is widely accepted and applied in multi-classifier fusion to express the importance of individual classifiers and the interaction among classifiers. In this fusion model, there are two keys to determine. One is determining the fuzzy measure. Many researchers have done much work and proposed many types of fuzzy measure and methods to determine fuzzy measures. Another is selecting from four types of fuzzy integral: Sugeno integral, Choquet integral, upper integral and lower integral. Usually, the type of fuzzy integral is specified in advance. Choquet integral is often the choice. This paper is to compare comprehensively four fuzzy integrals in multiple-classifier fusion and hope to give the foundation for selecting Choquet integral. According the theoretical and experimental analysis, it gives the conclusion that Choquet integral is the best suitable for classifier fusion. © 2010 IEEE.

关键词： Integral equations

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Discretization of continuous-valued attributes in decision tree generation

Discretization of continuous-valued attributes in decision t...

引用

International Conference on machine learning and Cybernetics

作者： Li, Wen-Liagn Yu, Rui-Hua Wang, Xi-Zhao Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China Department of Information Technology Baoding University Baoding 071000 China

ISBN: (纸本)9781424465262

Decision tree is one of the most popular and widely used classification models in machine learning. The discretization of continuous-valued attributes plays an important role in decision tree generation. In this paper, we improve Fayyad's discretization method which uses the average class entropy of candidate partitions to select boundaries for discretization. Our method can reduce the number of candidate boundaries further. Here we also propose a generalized splitting criterion for cut point selection and prove that the cut points are always on boundaries when using this criterion. Along with the formal proof, we present empirical results that the decision trees generated by using such criteria are similar on several datasets from the UCI machine learning Repository. © 2010 IEEE.

关键词： Decision trees

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A personalized products selection assistance based on e-commerce machine learning

A personalized products selection assistance based on e-comm...

引用

Proceedings of 2004 International Conference on machine learning and Cybernetics

作者： Yang, Hong-Wei Pan, Zhi-Geng Wang, Xi-Zhao Xu, Bing China State Key Lab. of CAD and CG Zhejiang University Hangzhou 310027 China Machine Learning Center Fac. of Math. and Computer Science Hebei University Baoding 071002 China

ISBN: (纸本)0780384032

The prosperity of electronic commerce has changed the traditional trading behaviors. More and more people are willing to perform Internet shopping. At the same time, consumers experience information overload and look for help in selecting from an overwhelming array of products. In order to overcome such a problem one option is to develop a personalized online assistance to retrieve product information that really matters for the customers. In this paper, we present a method that combines the genetic algorithm and k nearest neighbor technology to reason about the customer's personal preferences from his/her profile and then provide the most appropriate products to meet his/her needs. Our experimental results are showing that our systems have a bright future.

关键词： Electronic commerce

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Optimization methods for resources allocation in real-time strategy games

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

引用

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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A Total Error Rate Multi-class Classification

A Total Error Rate Multi-class Classification

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Xizhao Wang Meng Zhang Shuxia Lu Xu Zhou Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

ISBN: (纸本)9781467317139

The total error rate (TER) has been presented as a minimum classification error model for the single-layer feedforward network (SLFN) learning. The TEE, which uses one-against-all (OAA) for multi-class classification, may cause unbalanced data set especially for large number of training data in multi-class classification and then often has a bad influence on the accuracy. This paper proposes a new method, called multi-class total error rate (MTER) to deal with this problem. The MTER, which uses a unified learning mode of regression and multi-class classification and minimizes the error rate for each class, can approximate any target functions. It implies that a balanced data set can be obtained and the training process can be simplified. Experiments show that MTER has a higher accuracy and lower computational complexity in comparison with some learning algorithms such as ELM and TER. The experiments also show that the MTER has a similar performance with LIBSVM.

关键词： Extreme learning machine One-against-all Tatal error rate Multi-class classification Support vector machine Phyla multi-class Global Error Dataset Error analysis learning algorithms Ecosystem models Elm TECR gene

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

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: (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 scalab.lity 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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