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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3165 条记录，以下是2741-2750 订阅

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An RBF network approach to flatness pattern recognition based on SVM learning

An RBF network approach to flatness pattern recognition base...

引用

5th international conference on machine learning and Cybernetics

作者： He, Hai-Tao Li, Nan Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

ISBN: (纸本)1424400600

In the traditional method of flatness pattern recognition known as neural network with a changing topological configuration, slow convergence and local minimum were observed. Moreover, the process of experimenting the initial parameters and structure of the neural network according to the experience before has been proved time-consuming and complex. In this paper, a new approach was proposed based on the structural equivalence of radial basis function (111117) network and Support Vector machines (SVM). the SMO algorithm was employed to obtain more optimal structure and initial parameters of RBF network, and then the BP algorithm was used to adjust RBF network slightly. the new approach with the advantages of SVM, such as fast learning and whole optimization, was efficient and intelligent.

关键词： flatness pattern recognition SVM RBF network SMO algorithm

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Explaining winning poker -: A data mining approach

Explaining winning poker -: A data mining approach

引用

5th international conference on machine learning and Applications

作者： Johansson, Ulf Sonstrod, Cecilia Niklasson, Lars Univ Boras Sch Business & Informat Boras Sweden Univ Skovde Sch Humanities & Informat Skovde Sweden

ISBN: (纸本)9780769527352

this paper presents an application where machine learning techniques are used to mine data gathered from online poker in order to explain what signifies successful play. the study focuses on short-handed small stakes Texas Hold'em, and the data set used contains 105 human players, each having played more than 500 hands. Techniques used are decision trees and G-REX a rule extractor based on genetic programming. the overall result is that the rules induced are rather compact and have very high accuracy, thus providing good explanations of successful play. It is of course quite hard to assess the quality of the rules;i.e. if they provide something novel and non-trivial. the main picture is, however, that obtained rules are consistent with established poker theory. With this in mind, we believe that the suggested techniques will in future studies, where substantially more data is available, produce clear and accurate descriptions of what constitutes the difference between winning and losing in poker.

关键词： learning systems

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Design and implementation of modern elevator group control system

Design and implementation of modern elevator group control s...

引用

5th international conference on machine learning and Cybernetics

作者： Chen, Zhi-Ming Luo, Fei Xu, Yu-Ge Cao, Jian-Zhong South China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)1424400600

the design and implementation of a modern elevator group control system (EGCS) is introduced in this paper. the basic considerations of designing an EGCS are discussed, including related system parameters, evaluation criterions and traffic patterns. Least squares support vector machine algorithm is employed for traffic prediction. Using multi-support vector machine, the traffic pattern recognition is accomplished, then based on that recognition, the control strategies are generated. In the hall call assignment, the re-scheduling ability is achieved by the elevator suitability re-evaluation mechanism. A comparison with an older model demonstrates the effectiveness of the proposed EGCS.

关键词： elevator group control system traffic prediction traffic pattern recognition support vector machine hall call assignment

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Information classified recognition method based on fuzzy control

Information classified recognition method based on fuzzy con...

引用

5th international conference on machine learning and Cybernetics

作者： Ding, Shi-Fei Shi, Zhong-Zhi Zhu, Xi-Jun Shandong Agr Univ Coll Informat Sci & Engn Tai An 271018 Peoples R China Acad Sinica Comp Technol Inst Key Lab Intelligent Informat Proc Beijing 100080 Peoples R China Qingdao Technol Univ Sch Sci Qingdao 266033 Peoples R China

ISBN: (纸本)1424400600

In this paper, an information pattern recognition method based on fuzzy control is set up. On one hand, the modeling method of fuzzy information classified recognition pattern has been established. On the other hand, the data from Qufu City of Shandong Province during 14 years from 1990 to 2003 is processed and analyzed. the average temperature (degrees C) and rainfall (mm) in April each year are considered as the input of the system, a number of Aphis gossypii Glover (AGG) occurred for the Cotton in high period are considered as the output, Fuzzy information classified recognition pattern is set up in order to recognize the occurrence degree of the AGG the results of the returning recognition from 1990 to 2003 and the recognition for 2004 are satisfactory.

关键词： fuzzy control pattern recognition classified recognition pattern the Aphis gossypii Alover (AGG)

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A fast interactive sequential pattern mining algorithm based on memory indexing

A fast interactive sequential pattern mining algorithm based...

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5th international conference on machine learning and Cybernetics

作者： Ren, Jia-Dong Zong, Jun-Sheng Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

ISBN: (纸本)1424400600

the sequential pattern mining algorithm discovers all patterns meeting the user specified minimum support threshold. However, it is very impossibly that user could obtain the satisfactory patterns in just one query. the paper proposes a new interactive sequential pattern mining algorithm based on memory indexing, named MIFSPM, which adopts memory indexing technique, so it scans the sequence database only once to read data sequences into memory. Compact lattice frequent pattern tree (abbreviated as LFP-tree) saves previous results, in which the root node saves two minimum support thresholds. Besides, each node does not store frequent patterns and support information, but also index set mapped table (abbreviated as ISMT), except the root node. Rapidly, ISMT is used to mine new frequent sequential patterns without candidates generation. When to update the structure is decided by comparing the two minimum support thresholds, logistic information contained in the index set mapped table is used to fast mine new frequent sequential patterns without candidates generation. Experiments demonstrate the good performance and scalability of MIFSPM, with various minimum support thresholds. therefore, MIFSPM can mine frequent sequential patterns efficiently and be better than the other algorithms.

关键词： data mining sequential pattern memory indexing lattice frequent pattern tree index set mapped table

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Improving nearest neighbor classifier using Tabu Search and ensemble distance metrics

Improving nearest neighbor classifier using Tabu Search and ...

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6th IEEE international conference on data mining

作者： Tahir, Muhammad Atif Smith, James Univ W England Sch Comp Sci Bristol BS16 1QY Avon England

ISBN: (纸本)9780769527017

the nearest-neighbor (NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In this paper a new ensemble technique is proposed to improve the performance of NN classifier the proposed approach combines multiple NN classifiers, where each classifier uses a different distance function and potentially a different set of features (feature vector). these feature vectors are determined for each distance metric using Simple Voting Scheme incorporated in Tabu Search (TS). the proposed ensemble classifier with different distance metrics and different feature vectors (TS-DF/NN) is evaluated using various benchmark data sets from UCI machine learning Repository. Results have indicated a significant increase in the performance when compared with various well-known classifiers. Furthermore, the proposed ensemble method is also compared with ensemble classifier using different distance metrics but with same feature vector (with or without Feature Selection (FS)).

关键词： pattern recognition

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A new recognition method for the handwritten manchu character unit

A new recognition method for the handwritten manchu characte...

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5th international conference on machine learning and Cybernetics

作者： Zhang, Guang-Yuan Li, Jing-Jiao Wang, Ai-Xia Shenyang Univ Sch Informat Engn Shenyang 110044 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)1424400600

the Manchu character recognition method based on Manchu character unit is an efficient method. In this method, the recognition accuracy rate of Manchu character unit has great influence on the final recognition result. As new approach to solve this problem, a hybrid wavelet neural network scheme has developed as a recognition method replaces the original mini-distance method. Both the learning samples set and testing samples set are used, experimental results demonstrate the method based on the wavelet neural network is more efficient than the original mini-distance method.

关键词： wavelet neural network Manchu pattern recognition character recognition

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An adaptable time warping distance for time series learning

An adaptable time warping distance for time series learning

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5th international conference on machine learning and Applications

作者： Gaudin, Remi Nicoloyannis, Nicolas Univ Lyon 2 Lab ERIC F-69365 Lyon 07 France

ISBN: (纸本)9780769527352

Most machine learning and data mining algorithms for time series datasets need a suitable distance measure. In addition to classic p-norm distance, numerous other distance measures exist and the most popular is Dynamic Time Warping. Here we propose a new distance measure, called Adaptable Time Warping (ATW), which generalizes all previous time warping distances. We present a learning process using a genetic algorithm that adapts ATW in a locally optimal way, according to the current classification issue we have to resolve. It's possible to prove that ATW with optimal parameters is at least equivalent or at best superior to the other time warping distances for all classification problems. We show this assertion by performing comparative tests on two real datasets. the originality of this work is that we propose a whole learning process directly based on the distance measure rather than on the time series themselves.

关键词： learning systems

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Generic object recognition via integrating distinct features with SVM

Generic object recognition via integrating distinct features...

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5th international conference on machine learning and Cybernetics

作者： Huang, Tong-Cheng Ding, You-Dong Shanghai Univ Sch Engn & Comp Sci Shanghai 200072 Peoples R China Shaoyang Univ Dept Informat & Elect Engn Shaoyang 422000 Peoples R China

ISBN: (纸本)1424400600

In a genetic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different approaches. And these features are often separately selected and learned by machine learning methods. In this paper, the relation between distinct features obtained by different feature extraction approaches. and that for the same original images were studied by Kernel Canonical Correlation Analysis (KCCA). We apply a Support Vector machine (SVM) classifier in the learnt semantic space of the combined features and compare against SVM on the raw data and previously published state-of-the-art results. Experiments show that significant improvement is achieved with the SVM in the semantic space in comparison with direct SVM classification on the raw data.

关键词： KCCA SVM data fusion image recognition feature selection

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TARGET recognition USING BAYESIAN data FUSION MEthOD

TARGET RECOGNITION USING BAYESIAN DATA FUSION METHOD

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5th international conference on machine learning and Cybernetics

作者： Sun, Yu-Qiu Tian, Jin-Wen Liu, Jian Yangtze Univ Sch Informat & Math Jinzhou 434023 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Educ Commiss Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Dept Elect Informat & Energy State Educ Commiss Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

ISBN: (纸本)1424400600

Multisensor information plays an important pole in the target recognition and other application fields. Fusion performance is tightly depended on the fusion level selectes and the approach used. Feature level fusion is a potential and difficult fusion level. Bayesian fusion method is an important theory in feature level. A new method is presented to fuse infrared images and recognize object in the paper. Firstly, Bayesian principles, fusion mode and recognition decision function are described. then, aiming at the features of mid-wave infrared image and long-wave infrared image, we use Bayesian probability to fuse them. Last, recognize target and background obtained with training and test pattern vectors. the experiment results show stability and feasibility of the fusion recognition using Bayesian decision theory in infrared image.

关键词： Bayesian decision data fusion target recognition infrared images

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