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共 91 条记录，以下是31-40 订阅

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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...

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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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Potential support vector machine based on the reduced samples

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

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International Conference on Information Science and Engineering (ICISE)

作者： Shu-xia Lu Gui-en Cao Jie Meng Hua-chao Wang Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding Hebei China

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.

关键词： Support vector machines Classification algorithms Training Algorithm design and analysis Optimization Accuracy Acceleration

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Three-way decisions model based on rough fuzzy set

Three-way decisions model based on rough fuzzy set

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作者： Zhai, Junhai Zhang, Sufang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding071002 China Hebei Branch of China Meteorological Administration Training Centre China Meteorological Administration Baoding China

Three-way decisions model proposed by Yao gives a semantic interpretation of positive region, negative region and boundary region. This model was developed in the framework of classical rough set, the approached target concept is a crisp set, the employed knowledge is a equivalence relation. In this paper, we extend the three-way decisions model to rough fuzzy set. Specifically, the target concept is extended to a fuzzy set, while the used knowledge is also a equivalence relation. An example is given to illustrate the computation processes of the proposed three-way decisions model. The extended model can deal with the problems described by fuzzy decision tables with symbolic-valued conditional attributes and fuzzy decision attributes. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Fuzzy sets

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A fast positive-region reduction method based on dominance-equivalence relations

A fast positive-region reduction method based on dominance-e...

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2016 International Conference on machine learning and Cybernetics, ICMLC 2016

作者： Jin, Yongfei Li, Yan He, Qiang Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding Hebei Province071002 China School of Science Beijing University of Civil Engineering and Architecture Beijing102616 China

ISBN: (纸本)9781509003891

In this paper, we consider decision systems which consist of preference ordered conditional attributes and symbolic decision attributes. Thus, dominance relations and equivalence relations can be respectively defined on the conditional attribute set and decision attribute set. Based on this combined dominance-equivalence relations, a fast positive-region reduction (FPR) algorithm is developed by introducing the concept of positive approximation together with positive region reduction. The computation load of attribute reduction has been greatly reduced when compared with the original positive-region reduction algorithm (PR). Finally, the experimental results on 9 UCI data are demonstrated to make comparisons between the PR and the FPR method. © 2016 IEEE.

关键词： Approximation algorithms

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A Total Error Rate Multi-class Classification

A Total Error Rate Multi-class Classification

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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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Local, Mid-Level and Convolutional Features Fusion Using Multiple Kernel learning for Image Classification

Local, Mid-Level and Convolutional Features Fusion Using Mul...

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IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Yao Lu Hui Zhang Bojun Xie Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.

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Privacy issues in social networks: A brief survey

Privacy issues in social networks: A brief survey

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14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012

作者： Díaz, Irene Ralescu, Anca University of Oviedo Department of Computer Science E-33204 Oviedo Spain University of Cincinnati School of Computing Sciences and Informatics Machine Learning and Computational Intelligence Lab. Cincinnati OH 45221-0008 United States

ISBN: (纸本)9783642317231

Most social networks allow individuals to share their information with friends but also with unknown people. Therefore, in order to prevent unauthorized access to sensitive, private information, the study of privacy issues in social networks has become an important task. This paper provides a brief overview of the emerging research in privacy issues in social networks. © 2012 Springer-Verlag.

关键词： Data mining

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Unsupervised feature selection based on feature relevance

Unsupervised feature selection based on feature relevance

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Feng Zhang Ya-Jun Zhao Jun-Fen Key Lab. of Machine Learning and computational Intelligence Hebei University Baoding China College of Physics Science and Technology Hebei University Baoding China Key Lab. of Machine Learning and computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

Feature selection is an essential technique used in data mining and machine learning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is rarely studied. In this paper, we proposed an approach to select features for unsupervised problems. Firstly, the original features are clustered according to their relevance degree defined by mutual information. And then the most informative feature is selected from each cluster based on the contribution-information of each feature. The experimental results show that the proposed method can match some popular supervised feature selection methods. And the features selected by our method do include most of the information hidden in the overall original features.

关键词： machine learning Mutual information Random variables Entropy Unsupervised learning Information theory Cybernetics Educational institutions Data mining computational intelligence

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