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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1371-1380 订阅

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Extreme learning machine Approach for On-Line Voltage Stability Assessment

Extreme Learning Machine Approach for On-Line Voltage Stabil...

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4th international conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Duraipandy, P. Devaraj, D. Velammal Coll Engn & Technol Dept Elect & Elect Engn Velammal Tamil Nadu India Kalasalingam Univ Dept Comp Sci Engn Madras Tamil Nadu India

ISBN: (纸本)9783319037554;9783319037561

In recent years, voltage instability has become a major threat for the operation of many power systems. this paper proposes a scheme for on-line assessment of voltage stability of a power system for multiple contingencies using an Extreme learning machine (ELM) technique. Extreme learning machines are single-hidden layer feed-forward neural networks, where the training is restricted to the output weights in order to achieve fast learning with good performance. ELMs are competing with Neural Networks as tools for solving pattern recognition and regression problem. A single ELM model is developed for credible contingencies for accurate and fast estimation of the voltage stability level at different loading conditions. Loading margin is taken as the indicator of voltage instability. Precontingency voltage magnitudes and phase angles at the load buses are taken as the input variables. the training data are obtained by running Continuation Power Flow (CPF) routine. the effectiveness of the method has been demonstrated through voltage stability assessment in IEEE 30-bus system. To verify the effectiveness of the proposed ELM method, its performance is compared with the Multi Layer Perceptron Neural Network (MLPNN). Simulation results show that the ELM gives faster and more accurate results for on-line voltage stability assessment compared with the MLPNN.

关键词： Extreme learning machine loading margin voltage stability assessment

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Pedestrian detection based on incremental learning

Pedestrian detection based on incremental learning

引用

4th international conference on Intelligence Science and Big data Engineering, IScIDE 2013

作者： Xia, Yu Huang, Yongzhen Wang, Liang Geng, Xin School of Computer Science and Engineering Southeast University Nanjing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783642420566

Pedestrian detection is a hot topic in computer vision and pattern recognition. Existing pedestrian detection methods face new challenges in the background of big data, e.g., heavy burdens on computing and memory. To solve these problems, in this paper, we propose a pedestrian detection framework based on incremental learning. Compared with existing pedestrian detection frameworks, it costs much less time and memory. In addition, the performance of our framework is very close to the one which uses all training samples at once. Furthermore, with more new training samples, the performance can be enhanced continually with little time and memory, showing the potential in practical applications. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Big data

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Semi-supervised self-organizing feature map for gene expression data classification

Semi-supervised self-organizing feature map for gene express...

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5th international conference on pattern recognition and machine Intelligence, PReMI 2013

作者： Roy, Moumita Law, Anwesha Ghosh, Susmita Department of Computer Science and Engineering Jadavpur University Kolkata 700032 India

ISBN: (纸本)9783642450617

In this article, a one-dimensional self-organizing feature map (SOFM) neural network integrated with semi-supervised learning is used to predict the class label of gene expression data under the scarcity of the labeled patterns. Iterative learning of the semi-supervised SOFM network is carried out using a few labeled patterns along with some selected unlabeled patterns. the unlabeled patterns, for which the maximum target value is greater than a threshold, are selected as the confident ones. Results are found to be encouraging. © Springer-Verlag 2013.

关键词： Self organizing maps

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Remaining Useful Life Prediction for Rolling Element Bearing Based on Ensemble learning

Remaining Useful Life Prediction for Rolling Element Bearing...

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4th IEEE international conference on Prognostics and System Health Management (PHM)

作者： Zhang, Bin Zhang, Lijun Xu, Jinwu Univ Sci & Technol Beijing Natl Ctr Mat Serv Safety Beijing 100083 Peoples R China Beijing Univ Sci & Technol Sch Mech Engn Beijing 100083 Peoples R China

ISBN: (纸本)9788895608242

Information fusion is becoming state-of-the-art methodology for performance assessment of engineering assets. Efficiently and smartly combining multi-source information and relevant models from the interested object, more accurate and reliable diagnostic and prognostic results regarding the object can be achieved, which are especially significant for the condition-based maintenance and prognostics and health management applications. Ensemble learning, as a typical machine learning and decision fusion method, has long been applied in the pattern recognition field and demonstrated promising performance. However, scarce applications of ensemble learning have been found for remaining useful life (RUL) predictions. RUL prediction based on ensemble learning by merging multi-piece information and dynamically updating is proposed in this paper. Specifically, multiple base learners are trained to work as one RUL estimator and weighted averaging with dynamically updated weights upon the latest condition monitoring information is employed to aggregate these RULs to form the final RUL. Rolling element bearing degradation experimental data is used to verify and validate the effectiveness of the proposed method.

关键词： Roller bearings Condition monitoring Forecasting learning systems pattern recognition Condition based maintenance Decision fusion methods Monitoring information Multi source informations Performance assessment Prognostics and health managements Remaining useful life predictions Rolling Element Bearing

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A Bayesian Algorithm for Image-Based Time-to-Event Prediction

A Bayesian Algorithm for Image-Based Time-to-Event Predictio...

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4th international Workshop on machine learning in Medical Imaging (MLMI) in Conjunction with the 16th international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Sabuncu, Mert R. Harvard Univ Sch Med MGH AA Martinos Ctr Biomed Imaging Cambridge MA 02138 USA

ISBN: (纸本)9783319022673;9783319022666

this paper presents a novel Bayesian algorithm for making image-based predictions of the timing of a clinical event, such as the diagnosis of disease or death. We build on the Relevance Voxel machine (RVoxM) framework, a Bayesian multivariate prediction model that exploits the spatial smoothness in images and has been demonstrated to offer excellent predictive accuracy for clinical variables. We utilize the classical survival analysis approach to model the dynamic risk of the event of interest, while accounting for the limited follow-up-time, i.e. censoring of the training data. We instantiate the proposed algorithm (RVoxM-S) to analyze cortical thickness maps derived from structural brain Magnetic Resonance Imaging (MRI) data. We train RVoxM-S to make predictions about the timing of the conversion from Mild Cognitive Impairment (MCI) status to clinical dementia of the Alzheimer type (or AD). Our experiments demonstrate that RVoxM-S is significantly better at identifying subjects at high risk of conversion to AD over the next two years, compared to a binary classification algorithm trained to discriminate converters versus non-converters.

关键词： Multivariate pattern Analysis Survival Models Time-to-event prediction MRI

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A Novel BCI Classifier Based on Autoregressive Model and Support Vector machine

A Novel BCI Classifier Based on Autoregressive Model and Sup...

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4th international conference on Manufacturing Science and Engineering (ICMSE 2013)

作者： Huang, Lu Gu, Jun Li, Ran Li, Xiangjun Yu, Hong Dalian Ocean Univ Coll Informat Engn Dalian 116023 Peoples R China

ISBN: (纸本)9783037856932

the efficient feature extraction and classification are very crucial for brain computer interface(BCI) system. In this paper, feature extraction and classification for P300, a kind of EEG characteristic potential, was conducted. After preprocessing EEG signals, we used autoregressive(AR) model for feature extraction, segmenting the selected EEG channel data and building AR model for each segment respectively. AR model coefficients were estimated by using least square method, and the estimated coefficient sequence constituted the feature vector. We applied support vector machine(SVM) for classification and experimented on real EEG dataset. the experimental results showed the proposed method had a good recognition accuracy, being worth researching in the field of BCI.

关键词： brain-computer interface(BCI) pattern classification autoregressive model(AR model) support vector machine(SVM)

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Extraction of disease events for a real-time monitoring system

Extraction of disease events for a real-time monitoring syst...

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4th Symposium on Information and Communication Technology, SoICT 2013

作者： Nguyen, Minh-Tien Nguyen, Tri-thanh Viet Nam Viet Nam

ISBN: (纸本)9781450324540

In this paper, we propose a method that uses both semantic rules and machine learning to extract infectious disease events in Vietnamese electronic news, which can be used in a real-time system of monitoring the spread of diseases. Our method contains two important steps: detecting disease events from unstructured data and extracting information of the disease events. the event detection uses semantic rules and machine learning to detect a disease event;in the later step, Name Entity recognition (NER), rules, and dictionaries are used to capture the event's information. the performance of detection step is ≈77, 33% (F-score) and the precision of extraction step is ≈91, 89%. these results are better that those of the experiments in which rules were not used. this indicates that our method is suitable for extracting disease events in Vietnamese text. Copyright © 2013 ACM.

关键词： data mining

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4th international Joint conference on Ambient Intelligence, AmI 2013

4th International Joint Conference on Ambient Intelligence, ...

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4th international Joint conference on Ambient Intelligence, AmI 2013

ISBN: (纸本)9783319044057

the proceedings contain 33 papers. the special focus in this conference is on Ambient Intelligence. the topics include: Measuring the effectiveness of user interventions in improving the seated posture of computer users;visibility of wearable sensors as measured using eye tracking glasses;towards a transfer learning-based approach for monitoring fitness levels;unsupervised learning in ambient assisted living for pattern and anomaly detection;correlating average cumulative movement and Barthel index in acute elderly care;object tracking AAL application and behaviour modelling for the elderly and visually impaired;system for supporting clinical professionals dealing with chronic disease patients;temporal issues in teaching robot behaviours in a knowledge-based sensorised home;empirical methods for evaluating properties of configuration planning algorithms;a portable and self-presenting robotic ecology HRI testbed;a comparative study of the effect of sensor noise on activity recognition models;a comparison of evidence fusion rules for situation recognition in sensor-based environments;in-network sensor data modelling methods for fault detection;non-intrusive identification of electrical appliances;personalized remotely monitored healthcare in low-income countries through ambient intelligence;a visual interface for deal making;computer-mediated human-architecture interaction;applying semantic web technologies to context modeling in ambient intelligence;context-aware systems and adaptive user authentication;an ontology-based context-aware mobile system for on-the-move tourists;modeling the urban context through the theory of roles;ubiquitous applications over networked femtocell;perspectives and application of OUI framework with SMaG interaction model;an open architecture to enhance pervasiveness and mobility of health care services;online learning based contextual model for mobility prediction and a mobile-based automation system for maintenance inspection and lifesavin

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Unsupervised Visual Changepoint Detection Using Maximum Mean Discrepancy

Unsupervised Visual Changepoint Detection Using Maximum Mean...

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10th international conference on Image Analysis and recognition (ICIAR)

作者： Diu, Michael Gangeh, Mehrdad Kamel, Mohamed S. Univ Waterloo Dept Elect & Comp Engn Ctr Pattern Anal & Machine Intelligence Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9783642390944;9783642390937

the need to quantify similarity between two groups of objects is prevalent throughout the signal processing world. Traditionally, measures such as the Kullback-Leibler divergence are employed, but these may require expensive computations of covariance or integrals. Maximum mean discrepancy is a modern distance measure that is computationally simpler - involving the inner product between the difference in means of two groups' feature distributions - yet statistically powerful, because these distributions are mapped into a high-dimensional, nonlinear feature space using kernels, whereupon the means are estimated via the Parzen estimator. We apply this metric and leverage several powerful data representations from the supervised image classification world, such as bag-of-visual-words and sparse combinations of SIFT descriptors, to locate scene change points in videos with promising results.

关键词： visual changepoint detection unsupervised learning maximum mean discrepancy scene boundary detection video indexing

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Hybrid Approach Using Rough Sets and Fuzzy Logic to pattern recognition Task

Hybrid Approach Using Rough Sets and Fuzzy Logic to Pattern ...

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8th international conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Zolnierek, Andrzej Majak, Marcin Wroclaw Univ Technol Fac Elect Dept Syst & Comp Networks PL-50370 Wroclaw Poland

ISBN: (纸本)9783642408465

In this paper a hybrid classifier construction using rough sets and fuzzy logic is presented. Nowadays, we tackle with many realistic multi-dimensional problems with continuous values and overlaps in the feature space which require sophisticated recognition algorithms. Many methods have been proposed in the literature to improve classification accuracy, but it is increasingly harder to build new classifier from the scratch. Instead, new fusion methods are proposed to overcome this problem. In our rough-fuzzy approach data pre-processing and crisp discretization have a significant impact on the final classification efficiency. To deal with the problem of finding the optimal cuts in the feature space a genetic algorithm was proposed. After the algorithm description, in this paper also simulation investigations using different datasets from UCI machine learning Repository are presented.

关键词： Rough Sets Fuzzy Logic Hybrid pattern recognition Algorithm

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