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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 1786 条记录，以下是1301-1310 订阅

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On selecting useful unlabeled data using Multi-View learning Techniques 4

On selecting useful unlabeled data using Multi-View Learning...

引用

4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Le, thanh-Binh Kim, Sang-Woon Department of Computer Engineering Myongji University Yongin449-728 Korea Republic of

ISBN: (纸本)9789897580765

In a semi-supervised learning approach, using a selection strategy, strongly discriminative examples are first selected from unlabeled data and then, together with labeled data, utilized for training a (supervised) classifier. this paper investigates a new selection strategy for the case when the data are composed of different multiple views: first, multiple views of the data are derived independently;second, each of the views are used for measuring corresponding confidences with which examples to be selected are evaluated;third, all the confidence levels measured from the multiple views are used as a weighted average for deriving a target confidence;this selecting-and-training is repeated for a predefined number of iterations. the experimental results, obtained using synthetic and real-life benchmark data, demonstrate that the proposed mechanism can compensate for the shortcomings of the traditional strategies. In particular, the results demonstrate that when the data is appropriately decomposed into multiple views, the strategy can achieve further improved results in terms of the classification accuracy.

关键词： learning algorithms

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Adaptive classification for person re-identification driven by change detection 4

Adaptive classification for person re-identification driven ...

引用

4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Pagano, C. Granger, E. Sabourin, R. Marcialis, G.L. Roli, F. Lab. d'Imagerie de Vision et d'Intelligence Artificielle École de Technologie Supérieure Université du Qué bec Montreal Canada Pattern Recognition and Applications Group Dept. of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

ISBN: (纸本)9789897580765

Person re-identification from facial captures remains a challenging problem in video surveillance, in large part due to variations in capture conditions over time. the facial model of a target individual is typically designed during an enrolment phase, using a limited number of reference samples, and may be adapted as new reference videos become available. However incremental learning of classifiers in changing capture conditions may lead to knowledge corruption. this paper presents an active framework for an adaptive multi-classifier system for video-to-video face recognition in changing surveillance environments. To estimate a facial model during the enrolment of an individual, facial captures extracted from a reference video are employed to train an individual-specific incremental classifier. To sustain a high level of performance over time, a facial model is adapted in response to new reference videos according the type of concept change. If the system detects that the facial captures of an individual incorporate a gradual pattern of change, the corresponding classifier(s) are adapted through incremental learning. In contrast, to avoid knowledge corruption, if an abrupt pattern of change is detected, a new classifier is trained on the new video data, and combined with the individual's previously-trained classifiers. For validation, a specific implementation is proposed, with ARTMAP classifiers updated using an incremental learning strategy based on Particle Swarm Optimization, and the Hellinger Drift Detection Method is used for change detection. Simulation results produced with Faces in Action video data indicate that the proposed system allows for scalable architectures that maintains a significantly higher level of accuracy over time than a reference passive system and an adaptive Transduction Confidence machine-kNN classifier, while controlling computational complexity.

关键词： Face recognition

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Employing Mobile Applications in Human-machine Interaction in Visual pattern recognition Research 17th

Employing Mobile Applications in Human-Machine Interaction i...

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2nd international conference on learning and Collaboration Technologies / 17th international conference on Human-Computer Interaction

作者： Schur, Amir Tappert, Charles C. Pace Univ Seidenberg Sch CSIS 1 Martine Ave White Plains NY 10606 USA

ISBN: (纸本)9783319213804;9783319213798

this study is part of the first author's continued dissertation research in human-machine interaction in visual pattern recognition. Previous research focused on evaluating human-machine interaction using a flower recognition tool. Initial research showed that human interaction in color recognition improved accuracy significantly. We then looked more deeply into various automated color recognition algorithms and ways of combining them with human feedback. Described here is the process of upgrading the initial system into a new mobile application using Appinventor. After data collection, models were built for various color spaces. Sharing this experience may help other researchers incorporating a human-computer interaction component into their work.

关键词： Human-computer interaction Visual object recognition pattern classification Feature extraction Appinventor Color space

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Metric learning in dimensionality reduction 4

Metric learning in dimensionality reduction

引用

4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Schulz, Alexander Hammer, Barbara CITEC Centre of Excellence Bielefeld University Bielefeld Germany

ISBN: (纸本)9789897580765

the emerging big dimensionality in digital domains causes the need of powerful non-linear dimensionality reduction techniques for a rapid and intuitive visual data access. While a couple of powerful non-linear dimensionality reduction tools have been proposed in the last years, their applicability is limited in practice: since a non-linear projection is no longer characterised by semantically meaningful data dimensions, the visual display provides only very limited interpretability which goes beyond mere neighbourhood relationships and, hence, interactive data analysis and further expert insight are hindered. In this contribution, we propose to enhance non-linear dimensionality reduction techniques by a metric learning framework. this allows us to quantify the relevance of single data dimensions and their correlation with respect to the given visual display;on the one side, this explains its most relevant factors;on the other side, it opens the way towards an interactive data analysis by changing the data representation based on the learned metric from the visual display.

关键词： data visualization

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Hyperspectral data classification based on local polynomial approximation 4

Hyperspectral data classification based on local polynomial ...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Abe, Bolanle Tolulope Jordaan, Jaco Tshwane University of Technology South Africa Department of Electrical Engineering Pretoria South Africa

ISBN: (纸本)9789897580765

Processing and classification of hyperspectral data into various class labels have several challenges due to image objects entrenchment in a single pixel and large data size that needs serious computation and huge memory. this work presence local polynomial approximation (LPA) method to process Washington DC Mall hyperspectral data set for image classification. the data generated from LPA are then classified using neural network (NN), support vector machine (SVM) and random forest (RF). the classification procedures are implemented in Waikato Environment for Knowledge Analysis (WEKA). To evaluate the results, the performer of the classifiers per class label are presented in metrics and the overall classification results are presented in tabular form. the Friedman statistical test is carried out on the classification results to establish the performance of each classifier on the processed data. the LPA method is evaluated on the datasets using the different classifiers to demonstrate its efficacy.

关键词： Polynomial approximation

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Strategies for Determining Effective Step Size of the Backpropagation Algorithm for On-Line learning 7

Strategies for Determining Effective Step Size of the Backpr...

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7th international conference of Soft Computing and pattern recognition (SoCPaR)

作者： Kaneda, Yuya Zhao, Qiangfu Liu, Yong Pei, Yan Univ Aizu Dept Comp & Informat Syst Aizu Wakamatsu Fukushima Japan

ISBN: (纸本)9781467393607

In this paper, we investigate proper strategies for determining the step size of the backpropagation (BP) algorithm for on-line learning. It is known that for off-line learning, the step size can be determined adaptively during learning. For on-line learning, since the same data may never appear again, we cannot use the same strategy proposed for off-line learning. If we do not update the neural network with a proper step size for on-line learning, the performance of the network may not be improved steadily. Here, we investigate four strategies for updating the step size. they are 1) constant, 2) random, 3) linearly decreasing, and 4) inversely proportional, respectively. the first strategy uses a constant step size during learning, the second strategy uses a random step size, the third strategy decreases the step size linearly, and the fourth strategy updates the step size inversely proportional to time. Experimental results show that, the third and the fourth strategies are more effective. In addition, compared with the third strategy, the fourth one is more stable, and usually can improve the performance steadily.

关键词： Decision Boundary Making Multilayer Perceptron Backpropagation Algorithm On-Line learning

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Local and Global Genetic Fuzzy pattern Classifiers 11th

Local and Global Genetic Fuzzy Pattern Classifiers

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11th international conference on machine learning and data Mining (MLDM)

作者： Davidsen, Soren Atmakuri Sreedevi, E. Padmavathamma, M. Sri Venkateswara Univ Dept Comp Sci Tirupati 517502 Andhra Pradesh India

ISBN: (纸本)9783319210247;9783319210230

Fuzzy pattern classifiers are a recent type of classifiers making use of fuzzy membership functions and fuzzy aggregation rules, providing a simple yet robust classifier model. the fuzzy pattern classifier is parametric giving the choice of fuzzy membership function and fuzzy aggregation operator. Several methods for estimation of appropriate fuzzy membership functions and fuzzy aggregation operators have been suggested, but considering only fuzzy membership functions with symmetric shapes found by heuristically selecting a "middle" point from the learning examples. Here, an approach for learning the fuzzy membership functions and the fuzzy aggregation operator from data is proposed, using a genetic algorithm for search. the method is experimentally evaluated on a sample of several public datasets, and performance is found to be significantly better than existing fuzzy pattern classifier methods. this is despite the simplicity of the fuzzy pattern classifier model, which makes it interesting.

关键词： Approximate reasoning pattern recognition Fuzzy classifier Genetic algorithm

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Adaptive traffic signal control of bottleneck subzone based on Grey Qualitative Reinforcement learning algorithm 4

Adaptive traffic signal control of bottleneck subzone based ...

引用

4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Xiang, Junping Chen, Zonghai Department of Automation University of Science and Technology of China Hefei China Lianyungang JARI Electronics Co. Ltd. CSIC Lianuyungang China

ISBN: (纸本)9789897580772

A Grey Qualitative Reinforment learning algorithm is present in this paper to realize the adaptive signal control of bottleneck subzone, which is described as a nonlinear optimization problem. In order to handle the uncertainites in the traffic flow system, grey theory model and qualitative method were used to express the sensor data. In order to avoid deducing the function relationship of the traffic flow and the timing plan, grey reinforcement learning algorithm, which is the biggest innovation in this paper, was proposed to seek the solution. In order to enhance the generalization capability of the system and avoid the "curse of dimensionality" and improve the convergence speed, BP neural network was used to approximate the Q-function. We do three simulation experiments (calibrated with real data) using four evaluation indicators for contrast and analyze. Simulation results show that the proposed method can significantly improve the traffic situation of bottleneck subzone, and the algorithm has good robustness and low noise sensitivity.

关键词： Reinforcement learning

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learning dictionary via wavelet sparse principal component analysis 4

Learning dictionary via wavelet sparse principal component a...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Xie, Shengkun Lawniczak, Anna Krishnan, Sridhar Department of Electrical and Computer Engineering Ryerson University Toronto Canada Department of Mathematics and Statistics University of Guelph Guelph Canada

ISBN: (纸本)9789897580765

this work focuses on learning a double-sparse dictionary directly from specific data. the proposed method combines wavelet transform with sparse principal component analysis (SPCA), namely wavelet sparse PCA (WSPCA). the double-sparsity of the learned dictionary is achieved by obtaining both sparse signal components and sparse signal loadings. this work also examines the application of the proposed method to epileptic EEG signal classification problem. the nearly perfect classification accuracy (i.e., 99.7%) is obtained for a set of 8-hour long EEGs and the results are compared to other PCA-based sparse methods. Our experiments illustrate that the WSPCA approach lowers data variability of the extacted features, and it outperforms both sparse PCA and wavelet PCA methods in classifying the EEG signals that we consider.

关键词： Wavelet transforms

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Finger Vein recognition Using Integrated Responses of Texture Features 4

Finger Vein Recognition Using Integrated Responses of Textur...

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2015 4th international Work conference on Bioinspired Intelligence (IWOBI)

作者： Manmohan Kumar, R. Prem Agrawal, Rachit Sharma, Surbhi Dutta, Malay Kishore Travieso, Carlos M. Alonso-Hernandez, Jesus B. Amity Univ Amity Sch Engn & Technol Noida India Univ Las Palmas Gran Canaria Signal & Commun Dept Las Palmas Gran Canaria Spain

ISBN: (纸本)9781467378468

the finger vein recognition system is a secure and a reliable system with the advantage of robustness against malicious attacks. It is more convenient to operate this biometric feature than other biometric features such as facial and iris recognition system. the paper proposes a unique technique to find the local and the global features using Integrated Responses of Texture (IRT) features from finger veins which improves the overall accuracy of the system and is invariant to rotations. the segmentation of region of interest at different resolution levels makes the system highly efficient. the lower resolution data gives the overall global features and the higher resolution data gives the distinct local features. the complete feature set is descriptive in nature and reduces the Equal Error Rate to 0.523%. the Multi-Support Vector machine (Multi-SVM) is used to classify and match the obtained results. the experimental results indicate that the system is highly accurate with an accuracy of 94%.

关键词： Pyramid Levels Local Binary pattern Integrated Responses Multi-Support Vector machine (Multi- SVM)

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