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共 3357 条记录，以下是2551-2560 订阅

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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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Sentiment analysis of Facebook statuses using Naive Bayes Classifier for language learning

Sentiment analysis of Facebook statuses using Naive Bayes Cl...

引用

4th international conference on Information, Intelligence, Systems and Applications, IISA 2013

作者： Troussas, Christos Virvou, Maria Espinosa, Kurt Junshean Llaguno, Kevin Caro, Jaime Department of Informatics University of Piraeus Piraeus Greece Department of Computer Science University of the Philippines Cebu city Philippines

ISBN: (纸本)9781479907717

the growing expansion of contents, placed on the Web, provides a huge collection of textual resources. People share their experiences, opinions or simply talk just about whatever concerns them online. the large amount of available data attracts system developers, studying on automatic mining and analysis. In this paper, the primary and underlying idea is that the fact of knowing how people feel about certain topics can be considered as a classification task. People's feelings can be positive, negative or neutral. A sentiment is often represented in subtle or complex ways in a text. An online user can use a diverse range of other techniques to express his or her emotions. Apart from that, s/he may mix objective and subjective information about a certain topic. On top of that, data gathered from the World Wide Web often contain a lot of noise. Indeed, the task of automatic sentiment recognition in online text becomes more difficult for all the aforementioned reasons. Hence, we present how sentiment analysis can assist language learning, by stimulating the educational process and experimental results on the Naive Bayes Classifier. © 2013 IEEE.

关键词： Sentiment analysis

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PLAR: Parallel Large-scale Attribute Reduction on Cloud Systems

PLAR: Parallel Large-scale Attribute Reduction on Cloud Syst...

引用

14th international conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Zhang, Junbo Li, Tianrui Pan, Yi Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

ISBN: (纸本)9781479924189

Attribute reduction for big data is viewed as an important preprocessing step in the areas of pattern recognition, machine learning and data mining. In this paper, a novel parallel method based on MapReduce for large-scale attribute reduction is proposed. By using this method, several representative heuristic attribute reduction algorithms in rough set theory have been parallelized. Further, each of the improved parallel algorithms can select the same attribute reduct as its sequential version, therefore, owns the same classification accuracy. An extensive experimental evaluation shows that these parallel algorithms are effective for big data.

关键词： Attribute Reduction Rough Set theory MapReduce 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...

引用

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...

引用

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...

引用

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...

引用

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 latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

Extraction of latent patterns and contexts from social hones...

引用

11th IEEE international conference on Pervasive Computing and Communications, PerCom 2013

作者： Nguyen, thuong Phung, Dinh Gupta, Sunil Venkatesh, S. Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467345750

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. this is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data. © 2013 IEEE.

关键词： Ubiquitous computing

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An integrated framework for suicide risk prediction 13

An integrated framework for suicide risk prediction

引用

19th ACM SIGKDD international conference on Knowledge Discovery and data mining, KDD 2013

作者： Trany, Truyen Phung, Dinh Luo, Wei Harvey, Richard Berk, Michael Venkatesh, Svetha Pattern Recognition and Data Analytics Deakin University Australia Department of Computing Curtin University Australia Mental Health Drugs and Alcohol Barwon HealthVIC Australia School of Medicine Deakin University Australia

ISBN: (纸本)9781450321747

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient's clinical history into a temporal image to which a bank of one-side filters are applied. the responses are then partly transformed into mid-level features and then selected in 1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners. Copyright © 2013 ACM.

关键词： Filter banks

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4th international conference on Information Computing and Applications, ICICA 2013

4th International Conference on Information Computing and Ap...

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4th international conference on Information Computing and Applications, ICICA 2013

ISBN: (纸本)9783642537028

the proceedings contain 63 papers. the special focus in this conference is on Information Computing and Applications. the topics include: data fusion algorithm based on ultrasonic sensor network;research on OFDM technology in 4G;network resource search system based on cluster architecture;network sharing mode in the university libraries;interaction multi-station network based on android platform;driving program design of wireless sensor node;using behavior of social network sites based on acceptance model;detection and service security mechanism of XML injection attacks;web database based on data mining;training data of city tunnel traffic situation awareness;arbitrary waveform generator and total distortion evaluation;synchronous analysis of electroencephalogram based on nonlinear independence;MIDI music generator based on STM32;operating policy of short-term power dispatch for a wind farm;mechanical virtual experiment based on flash technology;digital library based on cloud computing;design of second language acquisition mobile learning resources;secondary developments based on BIM virtual technology;three-dimensional modeling technology in virtual reality;scene matching techniques;networked intelligent retrieval system based on semantic;design and implementation of computer immune system;incremental updating algorithm of weighted negative association rules;research of intelligent evacuation system;system model of university finance based on quantitative decision-making process and chain interaction;study on safety and reliability of logistics network system;research on integration of information flow and logistics based on TPL;study on data mining in digital libraries;poultry disease data mining platform based on strategy pattern;research on rule of project risk transfer;research on real time data warehouse architecture;regional road network shortest path algorithm in vehicle navigation systems;DM data mining based on improved apriori algorithm;modeling and simul

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