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检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1778 条记录，以下是1521-1530 订阅

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Assessment of contamination condition of insulator based on PSO-SVM

Assessment of contamination condition of insulator based on ...

引用

IEEE conference on Industrial Electronics and Applications (ICIEA)

作者： Shangbin Jiao Ding Liu Guo Xie Yi Deng Department of Automation of Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9781424427994

Contamination grades assessment is the important content for the online monitoring system of insulator leakage current (LC). the difficult of assessment is the nonlinear relationship between the electric character variables of the LC, the environment factors and the contamination condition of insulator surface. In this paper, based on laboratory simulation experiments and field data, the parameters of support vector machine (SVM) is optimized by using particle swarm optimization (PSO) arithmetic, then the SVM pattern recognition model of assessment of the contamination grades is constructed. the method takes advantages of the minimum structure risk of SVM and the quickly globally optimizing ability of particle swarm, and the mapping relation between the root mean square (R.M.S.) of LC, the peak value of the LC, the amplitude and times of the pulses of the LC, temperature and humidity of environment and contamination grades may be setup quickly by learning from sample data. Experiment results show that the contamination condition assessment method is effective. then the insulator contamination condition online detection system is developed based on the assessment model.

关键词： Support vector machines Particle swarm optimization Leakage current Dielectrics and electrical insulation Surface contamination Laboratories Arithmetic pattern recognition Optimization methods Root mean square

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Mining bilingual data from the web with adaptively learnt patterns 09

Mining bilingual data from the web with adaptively learnt pa...

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Proceedings of the Joint conference of the 47th Annual Meeting of the ACL and the 4th international Joint conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2

作者： Long Jiang Shiquan Yang Ming Zhou Xiaohua Liu Qingsheng Zhu Microsoft Research Asia Beijing P.R. China Chongqing University Chongqing P.R. China

ISBN: (纸本)9781932432466

Mining bilingual data (including bilingual sentences and terms) from the Web can benefit many NLP applications, such as machine translation and cross language information retrieval. In this paper, based on the observation that bilingual data in many web pages appear collectively following similar patterns, an adaptive pattern-based bilingual data mining method is proposed. Specifically, given a web page, the method contains four steps: 1) preprocessing: parse the web page into a DOM tree and segment the inner text of each node into snippets; 2) seed mining: identify potential translation pairs (seeds) using a word based alignment model which takes both translation and transliteration into consideration; 3) pattern learning: learn generalized patterns with the identified seeds; 4) pattern based mining: extract all bilingual data in the page using the learned patterns. Our experiments on Chinese web pages produced more than 7.5 million pairs of bilingual sentences and more than 5 million pairs of bilingual terms, both with over 80% accuracy.

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Comparison of two modern pattern recognition methods

Comparison of two modern pattern recognition methods

引用

4th international conference on Intelligent Information Hiding and Multimedia Signal Processing

作者： Shi Xiaochun Dalian Jiaotong Univ Dept Machine Engn Dalian Liaoning Prov Peoples R China

ISBN: (纸本)9780769532783

Two methods of pattern recognition are introduced in this paper: Unsupervised learning algorithm-fuzzy clustering method and supervised learning algorithm -neural network. the pattern recognition becomes failure pattern recognition if it is used in the fault diagnosis of the machine. Both merits and shortages of these two methods are discussed through a specific example in the mechanical faults diagnosis.

关键词： pattern recognition fuzzy clustering method neural network

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Research on Intrusion Detection System based on pattern recognition

Research on Intrusion Detection System based on pattern reco...

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4th international conference on Networked Computing and Advanced Information Management

作者： Zhu, Youchan Zheng, Ying N China Elect Power Univ Chengdu Peoples R China

ISBN: (纸本)9780769533223

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. this is a typical problem of the classfication,so intrusion detection(ID) can be seen as a pattern recognition problem. In this paper, In this paper, we build the intrusion detection system using Adaboost, a prevailing machine learning algorithm, construction detection classification. In the algorithm, decision RBF neural network are used as weak classifiers. For the training sets is multi-attribute non-linear and massive, we use pattern recognition method of non-linear datadimension reduction algorithm-Isomap algorithm to feature extraction and to improve the speed and training for the handling of classified speed In the feature extraction after the feature of the dimension and Adaboost algorithm training rounds, were studied and experimented. Finally, the experiment proved that Isomap and Adaboost combination of testing the effectiveness of the mothod.

关键词： Intrusion Detection (ID) Adaboost Isomap RBF

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Analysis of Binary Feature Mapping Rules for Promoter recognition in Imbalanced DNA Sequence datasets using Support Vector machine

Analysis of Binary Feature Mapping Rules for Promoter Recogn...

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4th international IEEE conference Intelligent Systems

作者： Damasevicius, Robertas Kaunas Univ Technol Software Engn Dept LT-51368 Kaunas Lithuania

ISBN: (纸本)9781424417391

recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments are promoters, Le., short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. In this paper, a machine learning method, called Support Vector machine (SVM), is used for classification of DNA sequences and promoter recognition. For optimal classification, 11 rules for mapping of DNA sequences into binary SVM feature space are analyzed. Classification is performed using a power series kernel function. Kernel parameters are optimized using a modification of the Nelder-Mead (downhill simplex) optimization method. the results of classification for drosophila and human sequence datasets are presented.

关键词： DNA mapping rules Support Vector machine data mining promoter recognition bioinformatics

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learning in Networks: Complex-valued Neurons, Pruning, and Rule Extraction

Learning in Networks: Complex-valued Neurons, Pruning, and R...

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4th international IEEE conference Intelligent Systems

作者： Zurada, Jacek M. Aizenberg, Igor Mazurowski, Maciej A. Univ Louisville Dept Elect & Comp Engn Louisville KY 40208 USA

ISBN: (纸本)9781424417391

this paper focuses on neural networks with complex-valued (CV) neurons as well as on selected aspects of neural networks learning, pruning and rule extraction. CV neurons can be used as versatile substitutes in real-valued perceptron networks. learning of CV layers is discussed in context of traditional multilayer feedforward architecture. Such learning is derivative-free and it usually requires networks of reduced size. Selected examples and applications of CV-networks in bioinformatics and pattern recognition are discussed. the paper also covers specialized learning techniques for logic rule extraction. Such techniques include learning with pruning, and can be used in expert systems, and other applications that rely on models developed to fit measured data.

关键词： Neural networks complex-valued neurons pruning rule extraction

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Self learning and its Implications for machine Understanding

Self Learning and its Implications for Machine Understanding

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4th international conference on Information and Automation for Sustainability

作者： Dinalankara, Wikum De Silva, Daswin Alahakoon, Damminda Monash Univ Cognit & Connectionist Syst Lab Clayton Vic 3800 Australia

ISBN: (纸本)9781424428991

Contemporary machine intelligence is far from realizing prominent hallmarks of human understanding and consciousness. the primary shortcoming of current methods can be attributed to the difficulty or implausibility of foreseeing and pre-programming each and every piece of information or knowledge. Emergent intelligence methods based on principles of self learning and self organization have been successful in infusing traits of understanding in machines. this understanding is in contrast to the constrained intelligence permeated on machines by classical approaches of intelligence following supervised knowledge acquisition mechanisms. the primary objective of this paper is to review current work in emergent intelligence methods and discuss means of orchestrating these in to a practical model that resembles the process of human understanding. the paper delineates intricacies of self-learning in humans from both biological and psychological perspectives. Following a discussion of several artificial models of the human mind that have been researched and documented at the conceptual level, we propose a comparatively pragmatic approach based on a novel unsupervised learning algorithm, the GSOM algorithm. this algorithm has been successfully applied to many real world knowledge acquisition and pattern discovery problems. the paper concludes with a further discussion of research developments in emergent systems, which we perceive to be the stepping stones in the search for true machine understanding.

关键词： artificial cognition self learning unsupervised learning growing self organising map algorithm multi-modal data fusion

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Mining Protein Primary Structure data Using Committee machines Approach to Predict Protein Contact Map

Mining Protein Primary Structure Data Using Committee Machin...

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4th international conference on Emerging Technologies (ICET)

作者： Habibi, Narjes K. Mahdaviani, Kaveh Saraee, Mohammad H. Isfahan Univ Technol Dept Elect & Comp Engn Adv Database Syst Data Min & Bioinformat Res Lab Esfahan *** Iran

ISBN: (纸本)9781424422104

Committee machines approach has shown to be useful in different applications. Protein primary structure data contain valuable information to extract. In this paper we mine these data and predict protein contact map based on committee machines. Contact map is the simplified, two dimensional representation of protein spatial structure. Contact map prediction is of great interest due to its application in fold recognition and predicting protein tertiary structure. the results show that the performance of the committee is considerably better than a single model.

关键词： artificial intelligence machine learning committee machine ensemble learning neural network bioinformatics protein contact map contact map prediction

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A comparative study of machine learning methods for detecting promoters in bacterial DNA sequences

引用

4th international conference on Intelligent Computing, ICIC 2008

作者： Tavares, Leonardo G. Lopes, Heitor S. Erig Lima, Carlos R. 80230-901 Brazil

ISBN: (纸本)3540859837

machine learning methods have been widely used in bioinformatics, mainly for data classification and pattern recognition. the detection of genes in DNA sequences is still an open problem. Identifying the promoter region laying prior the gene itself is an important aid to detect a gene. this paper aims at applying several machine learning methods to the construction of classifiers for detection of promoters in the DNA of Escherichia coli. A thorough comparison of methods was done. In general, probabilistic and neural network-based methods were those that performed better regarding accuracy rate. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Bioinformatics

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Artificial Immune recognition System for DNA Microarray data Analysis

Artificial Immune Recognition System for DNA Microarray Data...

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4th international conference on Natural Computation (ICNC 2008)

作者： Chen, Chuanhang Xu, Chuan Bie, Rongfang Gao, X. Z. Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Aalto Univ Dept Elect Engn FIN-02150 Espoo Finland

ISBN: (纸本)9780769533049

Artificial Immune Systems (AIS) are emerging information processing methods, which embody the principles of biological immune systems for tackling complex real-world problems. the Artificial Immune recognition System (AIRS) is a new kind of supervised learning AIS. the development of microarray technology has supplied a large volume of data for the prediction and diagnosis of cancer. Many popular machine learning techniques have been used in the microarray data analysis. In this paper, we apply AIRS to perform the microarray data classification based on an improved version of the information gain feature selection method three traditional classifiers have also been employed in our experiments for performance comparison. the results demonstrate the promising ability of AIRS in the microarray data analysis.

关键词： Immune system

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