IEEE Organizations related to Bagging

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Conferences related to Bagging

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Multimedia and Expo (ICME)

Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.

  • 2019 IEEE International Conference on Multimedia and Expo (ICME)

    speech, audio, image, video, text and new sensor signal processingsignal processing for media integration3D imaging, visualization and animationvirtual reality and augmented realitymulti-modal multimedia computing systems and human-machine interactionmultimedia communications and networkingmedia content analysis and searchmultimedia quality assessmentmultimedia security and content protectionmultimedia applications and servicesmultimedia standards and related issues

  • 2018 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities. ICME also features an Exposition of multimedia products and prototypes.

  • 2017 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to: – Speech, audio, image, video, text and new sensor signal processing – Signal processing for media integration – 3D visualization and animation – 3D imaging and 3DTV – Virtual reality and augmented reality – Multi-modal multimedia computing systems and human-machine interaction – Multimedia communications and networking – Media content analysis – Multimedia quality assessment – Multimedia security and content protection – Multimedia databases and digital libraries – Multimedia applications and services – Multimedia standards and related issues

  • 2016 IEEE International Conference on Multimedia and Expo (ICME)

    Topics of interest include, but are not limited to:- Speech, audio, image, video, text and new sensor signal processing- Signal processing for media integration- 3D visualization and animation- 3D imaging and 3DTV- Virtual reality and augmented reality- Multi-modal multimedia computing systems and human-machine interaction- Multimedia communications and networking- Media content analysis- Multimedia quality assessment- Multimedia security and content protection- Multimedia databases and digital libraries- Multimedia applications and services- Multimedia standards and related issues

  • 2015 IEEE International Conference on Multimedia and Expo (ICME)

    With around 1000 submissions and 500 participants each year, the IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2014 IEEE International Conference on Multimedia and Expo (ICME)

    The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications. In 2014, an Exposition of multimedia products, prototypes and animations will be held in conjunction with the conference.Topics of interest include, but are not limited to:

  • 2013 IEEE International Conference on Multimedia and Expo (ICME)

    To promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2012 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE Societies. It exchanges the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2011 IEEE International Conference on Multimedia and Expo (ICME)

    Speech, audio, image, video, text processing Signal processing for media integration 3D visualization, animation and virtual reality Multi-modal multimedia computing systems and human-machine interaction Multimedia communications and networking Multimedia security and privacy Multimedia databases and digital libraries Multimedia applications and services Media content analysis and search Hardware and software for multimedia systems Multimedia standards and related issues Multimedia qu

  • 2010 IEEE International Conference on Multimedia and Expo (ICME)

    A flagship multimedia conference sponsored by four IEEE societies, ICME serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2009 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2008 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.

  • 2007 IEEE International Conference on Multimedia and Expo (ICME)

  • 2006 IEEE International Conference on Multimedia and Expo (ICME)

  • 2005 IEEE International Conference on Multimedia and Expo (ICME)

  • 2004 IEEE International Conference on Multimedia and Expo (ICME)

  • 2003 IEEE International Conference on Multimedia and Expo (ICME)

  • 2002 IEEE International Conference on Multimedia and Expo (ICME)

  • 2001 IEEE International Conference on Multimedia and Expo (ICME)

  • 2000 IEEE International Conference on Multimedia and Expo (ICME)


2019 IEEE 15th International Conference on Control and Automation (ICCA)

The 15th IEEE International Conference on Control and Automation (IEEE ICCA 2019) will be held Tuesday through Friday, July 16-19, 2019, in Edinburgh, Scotland. The conference is jointly organized by IEEE Control Systems Chapter, Singapore, and IEEE Control Chapter for United Kingdom and Ireland. It is technically sponsored by IEEE Control Systems Society. It aims to create a forum for scientists and practising engineers throughout the world to present the latest research findings and ideas in the areas of control and automation, and possible contributions toward sustainable development and environment preservation. The conference is featured with the Best Paper Award and the Best Student Paper Award.


2019 International Conference on Computer Communication and Informatics (ICCCI)

The 2019 International Conference on Computer Communication and Informatics (ICCCI 2019)aims to provide an outstanding opportunity for both academic and industrial communities aliketo address new trends, challenges and emerging technologies on topics relevant to today's fastmoving areas of Computer, Communication and Informatics. The conference will feature invitedtalks and referred paper presentations. The vision of ICCCI 2019 is to develop fostercommunication among researchers and practitioners with a common interest but working in awide variety of areas in communication and informatics.

  • 2018 International Conference on Computer Communication and Informatics (ICCCI)

    The 2018 International Conference on Computer Communication and Informatics (ICCCI 2018) aims to provide an outstanding opportunity for both academic and industrial communities alike to address new trends and challenges and emerging technologies on topics relevant to today’s fast moving areas of Computer, Communication and Informatics. The conference will feature invited talks and referred paper presentations. The vision of ICCCI 2018 is to develop foster communication among researchers and practitioners working in a wide variety of areas in communication and informatics with a common interest.

  • 2017 International Conference on Computer Communication and Informatics (ICCCI)

    The 2017 International Conference on Computer Communication and Informatics (ICCCI) aimsto provide an outstanding opportunity for both academic and industrial communities alike toaddress new trends and challenges and emerging technologies on topics relevant to today'sfast moving areas of Computer, Communication and Informatics. The conference will featureinvited talks and referred paper presentations. The vision of ICCCI is to develop fostercommunication among researchers and practitioners working in a wide variety of areas incommunication and informatics with a common interest

  • 2016 International Conference on Computer Communication and Informatics

    The 2016 International Conference on Computer Communication and Informatics (ICCCI) aims to provide an outstanding opportunity for both academic and industrial communities alike to address new trends and challenges and emerging technologies on topics relevant to today's fast moving areas of Computer, Communication and Informatics. The conference will feature invited talks and referred paper presentations. The vision of ICCCI is to develop foster communication among researchers and practitioners working in a wide variety of areas in communication and informatics with a common interest

  • 2015 International Conference on Computer Communication and Informatics (ICCCI)

    The 2015 International Conference on Computer Communication and Informatics (ICCCI) aims to provide an outstanding opportunity for both academic and industrial communities alike to address new trends and challenges and emerging technologies on topics relevant to today

  • 2014 International Conference on Computer Communication and Informatics (ICCCI)

    ICCCI-2014 aims to provide an outstanding opportunity for both academic and industrial communities alike to address new trends and challenges in emerging technology.

  • 2013 International Conference on Computer Communication and Informatics (ICCCI)

    The 2013 IEEE International Conference on Computer Communication and Informatics (ICCCI 2013) aims to provide an outstanding opportunity for both academic and industrial communities alike to address new trends and challenges in emerging technology.

  • 2012 International Conference on Computer Communication and Informatics (ICCCI)

    The conference aims to provide an outstanding opportunity for both academic and industrial communities alike to address new trends and challenges and emerging technologies on topics relevant to today s fast moving areas of Computer Communication and Informatics. The conference will feature invited talks and referred paper presentations. The vision of this international conference is to faster communication among researchers and practitioners working in a wide variety of areas in Communication and Informatics with a common interest.


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Periodicals related to Bagging

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Most published Xplore authors for Bagging

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Xplore Articles related to Bagging

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Magemite: Character inputting system based on magnetic sensor

2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2015

We propose Magemite, a fine-grained input system that exploits the around device space (ADS) as an expansion of the limited input area. The key insight underlying Magemite is, magnetic sensor integrated in smart devices can sense nearby magnetic field strength. Using a permanent magnet, users could “write” in ADS to communicate with matched devices. Different from previous magnetic- sensing schemes ...


Comparison of AdaBoost.M2 and perspective based model ensemble in multispectral image classification

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017

AdaBoost is a popular ensemble method utilized in pattern recognition problems that are considered tough. Besides being a robust technique it does suffer from few limitations viz. size of training data and presence of noise in training data. In this context, we proposed a novel technique called Perspective Based Model (PBM) for ensemble creation in case of multispectral data analysis. ...


Feature-Based Subjectivity Classification of Filipino Text

2012 International Conference on Asian Language Processing, 2012

Subjectivity classification classifies whether a text expresses an opinion or not. Though there are already existing works in this field especially for the English Language, no reports have been made if these approaches are indeed effective when adapted to the Filipino language. This research reports a feature-based approach for subjectivity classification using existing classifiers such as Naïve Bayes, Bagging, Multilayer ...


Fuzzy clustering and fuzzy entropy based classification model

2010 6th International Conference on Emerging Technologies (ICET), 2010

In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and ...


High speed networks that preserve continuity and accuracy

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222), 2001

Developments in classification and regression like bagging, boosting and support vector machines tend to greatly improve generalization over simpler techniques, but may also result in longer computation times. In this paper we show one way of converting such computations into fast ones, without significant loss of accuracy, using a decision tree with piecewise linear approximants on the blocks.


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  • Magemite: Character inputting system based on magnetic sensor

    We propose Magemite, a fine-grained input system that exploits the around device space (ADS) as an expansion of the limited input area. The key insight underlying Magemite is, magnetic sensor integrated in smart devices can sense nearby magnetic field strength. Using a permanent magnet, users could “write” in ADS to communicate with matched devices. Different from previous magnetic- sensing schemes that recognize only coarse-grained gestures, Magemite can recognize user's fine-grained input like characters. However, individual's diverse writing patterns affect the recognition accuracy. To address this challenge, we preprocess the input trajectories and abstract different features of trajectories to uniquely identify user's input, then use these feature vectors to train several pattern recognition models for character recognition. We evaluate Magemite in various scenarios, and experimental results show Magemite can achieve average recognition accuracy over 85%.

  • Comparison of AdaBoost.M2 and perspective based model ensemble in multispectral image classification

    AdaBoost is a popular ensemble method utilized in pattern recognition problems that are considered tough. Besides being a robust technique it does suffer from few limitations viz. size of training data and presence of noise in training data. In this context, we proposed a novel technique called Perspective Based Model (PBM) for ensemble creation in case of multispectral data analysis. In the present paper, we evaluate its performance in terms of classification accuracy against AdaBoost.M2. Preliminary results show higher accuracy through PBM compared to a single classifier and promising classification results for PBM compared to AdaBoost.M2.

  • Feature-Based Subjectivity Classification of Filipino Text

    Subjectivity classification classifies whether a text expresses an opinion or not. Though there are already existing works in this field especially for the English Language, no reports have been made if these approaches are indeed effective when adapted to the Filipino language. This research reports a feature-based approach for subjectivity classification using existing classifiers such as Naïve Bayes, Bagging, Multilayer perceptron and Random Forest Tree. Result shows that the Bagging classifier gave the best results with 64.7% accuracy.

  • Fuzzy clustering and fuzzy entropy based classification model

    In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and named as Fuzzy Clustering and Fuzzy Entropy (FCFE) based classification model. With the help of FCM we obtained fuzzy membership matrix, revealing the underlying distribution and structure of the data. The Fuzzy entropy tells us about the degree of difficulty of classification of data. This information is used in sampling the training data into core sample and boundary sample. This sampling approach induces diversity in the ensemble which contributes to higher classification accuracy. The proposed method is evaluated on 4 UCI benchmark data sets with support vector machine (SVM) as the base classifier. The decision is combined using mean combiner rule. The results show that the proposed method delivers higher classification accuracy than stand alone SVM and the well known ensembles techniques of Bagging and Boosting.

  • High speed networks that preserve continuity and accuracy

    Developments in classification and regression like bagging, boosting and support vector machines tend to greatly improve generalization over simpler techniques, but may also result in longer computation times. In this paper we show one way of converting such computations into fast ones, without significant loss of accuracy, using a decision tree with piecewise linear approximants on the blocks.

  • Improving Sparse Recovery on Structured Images with Bagged Clustering

    The identification of image regions associated with external variables through discriminative approaches yields ill-posed estimation problems. This estimation challenge can be tackled by imposing sparse solutions. However, the sensitivity of sparse estimators to correlated variables leads to non- reproducible results, and only a subset of the important variables are selected. In this paper, we explore an approach based on bagging clustering- based data compression in order to alleviate the instability of sparse models. Specifically, we design a new framework in which the estimator is built by averaging multiple models estimated after feature clustering, to improve the conditioning of the model. We show that this combination of model averaging with spatially consistent compression can have the virtuous effect of increasing the stability of the weight maps, allowing a better interpretation of the results. Finally, we demonstrate the benefit of our approach on several predictive modeling problems.

  • A SNCCDBAGG-Based NN Ensemble Approach for Quality Prediction in Injection Molding Process

    This paper presents a SNCCDBAGG-based neural network (NN) ensemble approach for quality prediction in injection molding process. Bagging is used to create NNs for the ensemble by independently training these NNs on different training sets. Negative correlation learning via correlation-corrected data (NCCD) is used to achieve negative correlation of each network's error against errors for the rest of the ensemble by training transformed target data for NN in the ensemble as the desired network output for some epochs. A selection-based strategy is proposed to improve generalization ability when combining Bagging and NCCD. Experimental results show its good performance on quality predicting in injection molding process compared with single NN predictor and NCCD predictor.

  • Ensemble Neural Networks Using Interval Neutrosophic Sets and Bagging

    This paper presents an approach to the problem of binary classification using ensemble neural networks based on interval neutrosophic sets and bagging technique. Each component in the ensemble consists of a pair of neural networks trained to predict the degree of truth and false membership values. Uncertainties in the prediction are also estimated and represented using the indeterminacy membership values. These three membership values collectively form an interval neutrosophic set. In order to combine and classify outputs from components in the ensemble, the outputs of an ensemble are dynamically weighted and summed. The proposed approach has been tested with three benchmarking UCI data sets, which are ionosphere, pima, and liver. The proposed ensemble method improves the classification performance as compared to the simple majority vote and averaging methods which were applied only to the truth membership value. Furthermore, the results obtained from the proposed ensemble method also outperform the results obtained from a single pair of networks and the results obtained from a single truth network.

  • Signature verification using radial basis function classifier

    Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The Verification of handwritten Signature, which is a behavioral biometric, can be classified into off-line and online signature verification methods. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: online Signature Verification This paper addresses using ensemble approach of Radial Basis Function Classifier for online Signature Verification. Online signature verification, in general, gives a higher verification rate than off-line verification methods, because of its use of both static and dynamic features of problem space in contrast to off-line which uses only the static features. We show that proposed ensemble of Radial Basis Function classifier is superior to individual approach for Signature Verification in terms of classification rate.

  • Visual Object Recognition with Bagging of One Class Support Vector Machines

    A large number of training samples is requiredin developing visual object recognition systems. However, the size of samples is limited sometimes. This paper investigates bagging of one class support vector machines (OCSVM), which just use one class of objects for training. Experiments are performed on Caltech101 database. Our findings show that the performance with bagging method is better than single OCSVM. Furthermore, bagging of OCSVM can also keep better performance with limited number of training samples.



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