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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3281 条记录，以下是2531-2540 订阅

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A Neural Network Model for Online Multi-Task Multi-Label pattern recognition

A Neural Network Model for Online Multi-Task Multi-Label Pat...

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23rd international conference on Artificial Neural Networks (ICANN)

作者： Higuchi, Daisuke Ozawa, Seiichi Kobe Univ Grad Sch Engn Nada Ku Kobe Hyogo 6578501 Japan

ISBN: (纸本)9783642407277;9783642407284

This paper presents a new sequential multi-task learning model with the following functions: one-pass incremental learning, task allocation, knowledge transfer, task consolidation, learning of multi-label data, and active learning. This model learns multi-label data with incomplete task information incrementally. When no task information is given, class labels are allocated to appropriate tasks based on prediction errors;thus, the task allocation sometimes fails especially at the early stage. To recover from the misallocation, the proposed model has a backup mechanism called task consolidation, which can modify the task allocation not only based on prediction errors but also based on task labels in training data (if given) and a heuristics on multi-label data. The experimental results demonstrate that the proposed model has good performance in both classification and task categorization.

关键词： multi-task learning incremental learning neural networks multi-label recognition

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Using LS-SVM pattern recognizer to detect change-point in ARMA process

Using LS-SVM pattern recognizer to detect change-point in AR...

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3rd international conference on Frontiers of Manufacturing and Design Science (ICFMD 2012)

作者： Cheng Zhiqiang North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450011 Henan Peoples R China

ISBN: (纸本)9783037855782

Based on LS-SVM pattern recognizer, this paper develops an intelligent method for solving the problem of change-point detection, and the proposed model is applied to detect change-point of process mean-shift in auto-correlated time series process. In this research, LS-SVM algorithm and moving window method are used to detect the location of the mean shift signal, the LS-SVM pattern recognizer is designed and the performance of the recognizer is evaluated in terms of Accuracy Rate. Results of simulation experiment show that the proposed intelligent model is an effective method to detect change-point in ARMA data series.

关键词： change-points detection least squares support vector machine pattern recognition ARMA process

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Emerging Intelligent Computing Technology and Applications: 9th international conference, ICIC 2013, Nanning, China, July 28-31, 2013. Proceedings 1

引用

丛书名： Communications in Computer and Information Science 375

2013年

作者： Zhifeng Hao, Jinlong Huang, Ruichu Cai, Wen Wen (auth.), De-Shuang Huang, Phalguni Gupta, Ling Wang, Michael Gromiha (eds.)

ISBN: (数字)9783642396786

ISBN: (纸本)9783642396779;9783642396786

This book constitutes the refereed proceedings of the 9th international conference on Intelligent Computing, ICIC 2013, held in Nanning, China, in July 2013. The 192 revised full papers presented in the three volumes LNCS 7995, LNAI 7996, and CCIS 375 were carefully reviewed and selected from 561 submissions. The papers in this volume (CCIS 375) are organized in topical sections on Neural Networks; Systems Biology and Computational Biology; Computational Genomics and Proteomics; Knowledge Discovery and data mining; Evolutionary learning and Genetic Algorithms; machine learning Theory and Methods; Biomedical Informatics Theory and Methods; Particle Swarm Optimization and Niche Technology; Unsupervised and Reinforcement learning; Intelligent Computing in Bioinformatics; Intelligent Computing in Finance/Banking; Intelligent Computing in Petri Nets/Transportation Systems; Intelligent Computing in Signal Processing; Intelligent Computing in pattern recognition; Intelligent Computing in Image Processing; Intelligent Computing in Robotics; Intelligent Computing in Computer Vision; Special Session on Biometrics System and Security for Intelligent Computing; Special Session on Bio-inspired Computing and Applications; Computer Human Interaction using Multiple Visual Cues and Intelligent Computing; Special Session on Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.

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Face recognition based on Opposition Particle Swarm Optimization and Support Vector machine

Face Recognition based on Opposition Particle Swarm Optimiza...

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3rd IEEE international conference on Signal and Image Processing Applications (ICSIPA)

作者： Hasan, Mohammed Abdullah, Siti Norul Huda Sheikh Othman, Zulaiha Ali Univ Kebangsaan Malaysia Fac Informat Sci & Technol Pattern Recognit Res Grp Bandar Baru Bangi 43600 Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Data Min & Optimizat Grp Bandar Baru Bangi 43600 Malaysia

ISBN: (纸本)9781479902699;9781479902675

one of the most recently developed face recognition technique has utilized PSO-SVM, this method lacks in the initial phase of the PSO technique. That is in PSO;initially the populations are generated in random manner. Due to this random process, the population results may also be in random. Thus, it is not certain that this method will produce precise result. Hence to avoid this drawback, a modified face recognition method is proposed in this paper. Here, a new face recognition method based on Opposition based PSO with SVM (OPSO-SVM) is introduced. To accomplish the face recognition with our proposed OPSO-SVM, initially feature extraction process is carried out on the image database. In the feature extraction process, the efficient features are extracted and then given to the SVM training and testing process. In OPSO, the populations are generated in two ways: one is random population as same as the normal PSO technique and the other is opposition population, which is based on the random population values. The optimized parameters in SVM by OPSO efficiently perform the face recognition process. Two human face databases FERET and YALE are utilized to analyze the performance of our proposed OPSO-SVM technique and also this OPSO-SVM is compared with PSO-SVM and standard SVM techniques.

关键词： face recognition feature extraction particle swarm optimisation random processes support vector machines

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An Interactive Rough Set Attribute Reduction Using Great Deluge Algorithm

An Interactive Rough Set Attribute Reduction Using Great Del...

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3rd international Visual Informatics conference (IVIC)

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Univ Kebangsaan Malaysia Fac Informat Sci & Technol Data Min & Optimizat Res Grp DMO Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia

ISBN: (纸本)9783319029573;9783319029580

Dimensionality reduction from an information system is a problem of eliminating unimportant attributes from the original set of attributes while avoiding loss of information in data mining process. In this process, a subset of attributes that is highly correlated with decision attributes is selected. In this paper, performance of the great deluge algorithm for rough set attribute reduction is investigated by comparing the method with other available approaches in the literature in terms of cardinality of obtained reducts (subsets), time required to obtain reducts, number of calculating dependency degree functions, number of rules generated by reducts, and the accuracy of the classification. An interactive interface is initially developed that user can easily select the parameters for reduction. This user interface is developed toward visual data mining. The carried out model has been tested on the standard datasets available in the UCI machine learning repository. Experimental results show the effectiveness of the method especially with relation to the time and accuracy of the classification using generated rules. The method outperformed other approaches in M-of-N, Exactly, and LED datasets with achieving 100% accuracy.

关键词： interactive data mining great deluge algorithm rough set theory attribute reduction classification

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A Research on Navigation Star Catalog Selection Algorithm Based on SVM

A Research on Navigation Star Catalog Selection Algorithm Ba...

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3rd international conference on Mechatronics and Intelligent Materials (MIM 2013)

作者： Liu, Fucheng Liu, Zhaohui Liu, Wen Liang, Dongsheng Cui, Kai Yuan, Hui Xian Inst Opt & Precis Mech CAS Xian 710119 Peoples R China

ISBN: (纸本)9783037857106

A navigation star catalog (NSC) selection algorithm via support vector machine (SVM) is proposed in this paper. The sphere spiral method is utilized to generate the sampling boresight directions by virtue of obtaining the uniform sampling data. Then the theory of regression analysis methods is adopted to extract the NSC,I, and an evenly distributed and small capacity NSC is obtained. Two criterions, namely a global criterion and a local criterion, are defined as the uniformity criteria to test the performance of the NSC generated. Simulations show that, compared with MFM, magnitude weighted method (MWM) and self-organizing algorithm(S-OA), the Boltzmann entropy (B.e) of SVM selection algorithm (SVM-SA) is the minimum, to 0.00207. Simultaneously, under the conditions such as the same field of view (FOV) and elimination of the hole, both the number of guide stars (NGS) and standard deviation (std) of SVM-SA is the least, respectively 7668 and 2.17. Consequently, the SVM-SA is optimal in terms of the NGS and the uniform distribution, and has also a strong adaptability.

关键词： guide star catalog support vector machine star pattern recognition Boltzmann entropy

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Rule Extraction from a Modified FMM Neural Network for Sign Language recognition

Rule Extraction from a Modified FMM Neural Network for Sign ...

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The 3rd international conference on Emerging Trends in Computer and Image Processing(ICETCIP’2013);The 3rd international conference on Advancements in Electronics and Power Engineering(ICAEPE’2013);The 3rd international conference on Trends in Mechanical and Industrial Engineering(ICTMIE’2013);The 3rd international conference on Advances in Biotechnology and Pharmaceutical Sciences(ICABPS’2013);The 3rd international conference on Chemical,Biological and Environment Sciences(ICCEBS’2013);The 3rd international conference on Management,Economics and Social Sciences(ICMESS’2013)

作者： Ho-Joon Kim Seung-Kang Lee the Handong Global University

This paper presents a rule extraction method using a modified FMM neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. A modified hyperbox membership function is introduced and the learning algorithm with the model is presented in this paper. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.

关键词： Rule extraction, FMM neural network, sign language recognition, pattern classification

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3rd international Joint conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2011

3rd International Joint Conference on Knowledge Discovery, K...

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ISBN: (纸本)9783642371851

The proceedings contain 30 papers. The special focus in this conference is on Knowledge Discovery and Information Retrieval, Knowledge Engineering and Ontology Development and Knowledge Management and Information Sharing. The topics include: mining graphs of prescribed connectivity;an explorative study of proximity measures;comparing the macroeconomic responses of US and Japan through time series segmentation;a block coclustering model for pattern discovering in users' preference data;improving text retrieval accuracy by using a minimal relevance feedback;using distant supervision for extracting relations on a large scale;a fast method for web template extraction via a multi-sequence alignment approach;unsupervised terminology graph extraction and decomposition;learning to classify text using a few labeled examples;a system to support legal case building and reasoning;different approaches to build brief ontologies;ontology engineering for the autonomous systems domain;cloud services composition support by using semantic annotation and linked data;identifying services from a service provider and customer perspectives;code quality cultivation;misbehavior discovery through unified software-knowledge models;an universal indexing system approach;determining the collaboration maturity of organizational teams;assessing the impact of in-government cooperation dynamics and discovering the relevance of external context to business processes.

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international Joint conference, CISIS 2012, ICEUTE 2012, SOCO 2012 Special Sessions

International Joint Conference, CISIS 2012, ICEUTE 2012, SOC...

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5th Int. conference on Computational Intelligence in Security for Information Systems, CISIS 2012, In Conjuntion with: 7th international conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2012, and 3rd ICEUTE 2012

ISBN: (纸本)9783642330179

The proceedings contain 55 papers. The topics discussed include: automatic analysis of web service honeypot data using machine learning techniques;a security pattern-driven approach toward the automation of risk treatment in business processes;neural network ensembles design with self-configuring genetic programming algorithm for solving computer security problems;clustering for intrusion detection: network scans as a case of study;sensor networks security based on sensitive robots agents: a conceptual model;comments on a cryptosystem proposed by Wang and Hu;equivalent inner key recovery attack to NMAC;hybrid compression of the aho-corasick automaton for static analysis in intrusion detection systems;how political illusions harm national stability: fiscal illusion as a source of taxation;on fitness function based upon quasigroups power sequences;and usability of software intrusion-detection system in web applications.

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On the effect of data reduction on classification accuracy

On the effect of data reduction on classification accuracy

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international conference on Information Technology and e-Services (ICITeS)

作者： Syrine Ben Meskina Faculty of Sciences of Tunis Tunis El Manar University Tunisia

data reduction is an important pre-processing step to both supervised and unsupervised machine learning problems. In this paper, we investigate, in a first part, the two existing strategies for data reduction which are feature selection (FS) and dimensionality reduction (DR). In a second part, we study the impact of different data reduction methods on supervised machine learning in terms of classification accuracy and computational costs. In fact, we compare, in the one hand, the generated subsets of attributes by filter and wrapper algorithms as well as new variables constructed by two variants of a DR method. In the other hand, we compare the classification achieved on initial data set, reduced data sets and also on successively reduced size of the considered data sets.

关键词： Accuracy Classification algorithms Principal component analysis Filtering algorithms machine learning algorithms data mining Algorithm design and analysis

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