Conferences related to Kernel

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2016 IEEE International Conference on Image Processing (ICIP)

Signal processing, image processing, biomedical imaging, multimedia, video, multidemensional.


IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium

The Geoscience and Remote Sensing Society (GRSS) seeks to advance science and technology in geoscience, remote sensing and related fields using conferences, education and other resources. Its fields of interest are the theory, concepts and techniques of science and engineering as they apply to the remote sensing of the earth, oceans, atmosphere, and space, as well as the processing, interpretation and dissemination of this information.

  • IGARSS 2014 - 2014 IEEE International Geoscience and Remote Sensing Symposium

    GRSS seeks to advance science and technology in geoscience, remote sensing and related fields. IGARSS begins with a plenary session and tutorials on the most up-to-date topics. Paper, panel and poster sessions will be scheduled. The exhibit hall features the latest in geoscience instruments, equipment, software, publications, and scientific programs.

  • IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium

    GRSS seeks to advance science and technology in geoscience, remote sensing and related fields. IGARSS begins with a plenary session and tutorials on the most up-to-date topics. Paper, panel and poster sessions will be scheduled. The exhibit hall features the latest in geoscience instruments, equipment, software, publications, and scientific programs.

  • IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium

    Remote Sensing Techniques and Applications.

  • IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium

    To gather world-class scientists, engineers and educators engaged in the fields of geoscience and remote sensing to meet and present their latest activities. Nearly 1900 participants from all over the world attended technical sessions, tutorials, exhibits and social activities at the 2010 event in Hawaii.

  • IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium

    Remote Sensing techniques and applications


2013 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

Wavelet General Theory; Bases and Frames Theory; Filter Banks; Approximation Theory; Spline Theory; Time-Frequency Analysis; Integral Transforms; Visualization; Watermarking; Biometrics; Signal Estimation; Image Fusion; Image Recovery; Image Compression; Biomedical Imaging; Document Analysis; Learning Theory; Noise Reduction; Machine Vision; Classification; Feature Extraction; Emotion Computation; Cluster Analysis; Deformation Analysis; Descriptor of Shapes; Diagnosis of Faults; Intelligent Robot; Hand Gestures Classification; Image Indexing and Retrieval; Range Imaging and Detection; Seismological Signal Processing; Stochastic Pattern Recognition; Texture Analysis and Classification; Animation Image Analysis; Enhancement and Restoration; Handwritten and Printed Character Recognition; Analysis and Detection of Singularities; Artificial Life; Intelligent Control System; Intelligent Human Machine Interface; Information Safety

  • 2012 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

    Wavelet General Theory; Bases and Frames Theory; Filter Banks; Approximation Theory; Spline Theory; Time-Frequency Analysis; Integral Transforms; Visualization; Watermarking; Biometrics; Signal Estimation; Image Fusion; Image Recovery; Image Compression; Biomedical Imaging; Document Analysis; Learning Theory; Noise Reduction; Machine Vision; Classification; Feature Extraction; Emotion Computation; Cluster Analysis; Deformation Analysis; Descriptor of Shapes; Diagnosis of Faults; Intelligent Robot; Hand Gestures Classification; Image Indexing and Retrieval; Range Imaging and Detection; Seismological Signal Processing; Stochastic Pattern Recognition; Texture Analysis and Classification; Animation Image Analysis; Enhancement and Restoration; Handwritten and Printed Character Recognition; Analysis and Detection of Singularities; Artificial Life; Intelligent Control System; Intelligent Human Machine Interface; Information Safety.

  • 2011 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

    Wavelet General Theory; Bases and Frames Theory; Filter Banks; Approximation Theory; Spline Theory; Time-Frequency Analysis; Integral Transforms; Visualization; Watermarking; Biometrics; Signal Estimation; Image Fusion; Image Recovery; Image Compression; Biomedical Imaging; Document Analysis; Learning Theory; Noise Reduction; Machine Vision; Classification; Feature Extraction; Emotion Computation; Cluster Analysis; Deformation Analysis; Descriptor of Shapes; Diagnosis of Faults; Intelligent Robot.

  • 2010 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

    Wavelet General Theory; Bases and Frames Theory; Filter Banks; Approximation Theory; Spline Theory; Time-Frequency Analysis; Integral Transforms; Visualization; Watermarking; Biometrics; Signal Estimation; Image Fusion; Image Recovery; Image Compression; Biomedical Imaging; Document Analysis; Learning Theory; Noise Reduction; Machine Vision; Classification; Feature Extraction; Emotion Computation; Cluster Analysis; Deformation Analysis; Descriptor of Shapes; Diagnosis of Faults; Intelligent Robot; Hand Gest


2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas)

Neural Networks

  • 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane)

    The annual IJCNN is the premier international conference in the field of neural networks.

  • 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose)

    IJCNN 2011 will include paper presentations, tutorials, workships, panels, special sessions and competitions on topics related to neural networks, including: Neural network theory and models; neural network applications; computational neuroscience; neurocognitive models; neuroengineering; neuroinformatics; neuroevolution; collective intelligence; embodied robotics; artificial life, etc.

  • 2010 International Joint Conference on Neural Networks (IJCNN 2010 - Barcelona)

  • 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta)

    IJCNN is the premier international conference in the area of neural networks theory, analysis and applications. It is organized by the International Neural Networks Society (INNS) and sponsored jointly by INNS and the IEEE Computational Intelligence Society. This is an exemplary collaboration between the two leading societies on neural networks and it provides a solid foundation for the future extensive development of the field.

  • 2008 International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong)

    The IJCNN is the premier event in the field of neural networks. It covers all topics in neural network research (broadly defined).

  • 2007 International Joint Conference on Neural Networks (IJCNN 2007 - Orlando)

    The IJCNN is the premier event in the field of neural networks. It covers all topics in neural network research (broadly defined).


2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

he International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established as a biannual conference in 1991 and became an annual event in 2007. It is the premiere conference of the Australian Pattern Recognition Society (APRS).

  • 2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

    DICTA is the premiere conference of the Australian Pattern Recognition Society (APRS) focusing on Image Processing, Computer Vision, Pattern Recognition and related areas. It is a major outlet for published work in these fields and one of the main vehicles for engagement between the Australian research community and industry.

  • 2010 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

    (DICTA) is the main Australian conference on machine vision, image processing, pattern recognition and related areas. Since its establishment, DICTA has been a biannual meeting. In 2008, it turned into an annual conference. It is the conference of the Australian Pattern Recognition Society (APRS).

  • 2009 Digital Image Computing: Techniques and Applications (DICTA)

    DICTA is the main Australian conference on digital image processing, machine vision and related areas. DICTA2009 will be the eleventh meeting hosted by Australian Pattern Recognition Society (APRS). This conference typically attracts 120 participants from Australia and the Asia Pacific regions as well as many international researchers.

  • 2007 Digital Image Computing Techniques and Applications DICTA

    DICTA is hosted by the Australian Pattern Recognition Society and is held in odd numbered years. DICTA typically attracts 150 participants including many international researchers. DICTA 2007 is the ninth meeting of this conference and comprise international keynote speakers, parallel sessions of contributed papers, and posters. The conference will be run as a DEST category E1 conference, including double peer review of full papers.


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Periodicals related to Kernel

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Image Processing, IEEE Transactions on

Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.


Neural Networks, IEEE Transactions on

Devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks.


Signal Processing Letters, IEEE

Rapid dissemination of new results in signal processing world-wide.


Systems, Man, and Cybernetics, Part B, IEEE Transactions on

The scope of the IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or between machines, humans, and organizations. The scope of Part B includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, ...




Xplore Articles related to Kernel

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A Novel Method for Video Shot Similarity Measures

Li Deng; Li-zuo Jin; Shu-min Fei 2006 International Conference on Machine Learning and Cybernetics, 2006

In this paper, a novel method is proposed to determine the similarity between video shots. A video shot is treated as an ensemble that consists of multiple video key frames, so the shot similarity can be measured by the ensemble similarity. Based on nonlinear mapping, the original space is mapped to a high dimension space where the ensemble distribution can ...


Capturing age-related alternations in the human smooth pursuit mechanism by Volterra models

Alexander Medvedev; Daniel Jansson 2016 American Control Conference (ACC), 2016

A method for quantifying the effects of aging in the human smooth pursuit system is proposed. The dynamical properties of the oculomotor system are characterized by means of a truncated Volterra model that has previously been utilized for distinguishing between patients diagnosed with Parkinson's disease and healthy controls. The orthonormal basis of Laguerre functions is employed for the parameterization of ...


Joint loop mapping and data placement for coarse-grained reconfigurable architecture with multi-bank memory

Shouyi Yin; Xianqing Yao; Tianyi Lu; Leibo Liu; Shaojun Wei 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2016

Coarse-Grained Reconfigurable Architecture (CGRA) is a promising architecture with high performance, high power-efficiency and attraction of flexibility. The compute-intensive parts of an application (e.g. loops) are often mapped onto CGRA for acceleration. Since the high-parallel demands of PEs and the extremely expensive cost of single-bank memory with multi-port, the architecture with multi-bank memory is favored increasingly. Based on this purpose, ...


Selecting Graph Cut Solutions via Global Graph Similarity

Canh Hao Nguyen; Nicolas Wicker; Hiroshi Mamitsuka IEEE Transactions on Neural Networks and Learning Systems, 2014

Graph cut is a common way of clustering nodes on similarity graphs. As a clustering method, it does not give a unique solution under usually used loss functions. We specifically show the problem in similarity graph-based clustering setting that the resulting clusters might be even disconnected. This is counter-intuitive as one wish to have good clustering solutions in the sense ...


Improved background modeling through color de-correlation

Jong Geun Park; Chulhee Lee 2011 19th European Signal Processing Conference, 2011

Background modelling and foreground detection, which significantly affect the performance of intelligent visual surveillance systems, are challenging works due to dynamic background, illumination changes, image artefacts, etc. This paper describes an improved algorithm for background modelling. A pixel-wise non-parametric statistical model of the HSV colour components and gradients is used for background modelling. Since the non-parametric statistical model using the ...


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Educational Resources on Kernel

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eLearning

A Novel Method for Video Shot Similarity Measures

Li Deng; Li-zuo Jin; Shu-min Fei 2006 International Conference on Machine Learning and Cybernetics, 2006

In this paper, a novel method is proposed to determine the similarity between video shots. A video shot is treated as an ensemble that consists of multiple video key frames, so the shot similarity can be measured by the ensemble similarity. Based on nonlinear mapping, the original space is mapped to a high dimension space where the ensemble distribution can ...


Capturing age-related alternations in the human smooth pursuit mechanism by Volterra models

Alexander Medvedev; Daniel Jansson 2016 American Control Conference (ACC), 2016

A method for quantifying the effects of aging in the human smooth pursuit system is proposed. The dynamical properties of the oculomotor system are characterized by means of a truncated Volterra model that has previously been utilized for distinguishing between patients diagnosed with Parkinson's disease and healthy controls. The orthonormal basis of Laguerre functions is employed for the parameterization of ...


Joint loop mapping and data placement for coarse-grained reconfigurable architecture with multi-bank memory

Shouyi Yin; Xianqing Yao; Tianyi Lu; Leibo Liu; Shaojun Wei 2016 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2016

Coarse-Grained Reconfigurable Architecture (CGRA) is a promising architecture with high performance, high power-efficiency and attraction of flexibility. The compute-intensive parts of an application (e.g. loops) are often mapped onto CGRA for acceleration. Since the high-parallel demands of PEs and the extremely expensive cost of single-bank memory with multi-port, the architecture with multi-bank memory is favored increasingly. Based on this purpose, ...


Selecting Graph Cut Solutions via Global Graph Similarity

Canh Hao Nguyen; Nicolas Wicker; Hiroshi Mamitsuka IEEE Transactions on Neural Networks and Learning Systems, 2014

Graph cut is a common way of clustering nodes on similarity graphs. As a clustering method, it does not give a unique solution under usually used loss functions. We specifically show the problem in similarity graph-based clustering setting that the resulting clusters might be even disconnected. This is counter-intuitive as one wish to have good clustering solutions in the sense ...


Improved background modeling through color de-correlation

Jong Geun Park; Chulhee Lee 2011 19th European Signal Processing Conference, 2011

Background modelling and foreground detection, which significantly affect the performance of intelligent visual surveillance systems, are challenging works due to dynamic background, illumination changes, image artefacts, etc. This paper describes an improved algorithm for background modelling. A pixel-wise non-parametric statistical model of the HSV colour components and gradients is used for background modelling. Since the non-parametric statistical model using the ...


More eLearning Resources

IEEE-USA E-Books

  • Kernel Implementation of Sockets

    This chapter contains sections titled: Socket Layer VFS and Socket Protocol Socket Registration struct inet_protosw Socket Organization in the Kernel Socket inet_create Flow Diagram for Socket Call Summary

  • Kernel Implementation of TCP Connection Setup

    This chapter contains sections titled: Connection Setup Bind Listen Connection Request Handling by Kernel Accept Client Side Setup Summary

  • Index

    Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC- Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

  • Building Interpretable Systems in Real Time

    This chapter contains sections titled: Introduction Improving eTS Span in the Reachable State Space Pruning the Rule Base Kernel Machines Experimental Results Conclusion Acknowledgments References

  • Netlink Sockets

    This chapter contains sections titled: Introduction to Netlink Sockets Netlink Socket Registration and Initialization at Boot Time How Is the Kernel Netlink Socket Created? How Is the User Netlink Socket Created? Netlink Data Structures Other Important Data Strutures Netlink Packet Format Netlink Socket Example - tc Command for Adding a qdisc Flow Diagram for tc Command in Kernel Space Summary

  • References

    The Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This volume investigates how Machine Learning techniques can improve Statistical Machine Translation, currently at the forefront of research in the field. The book looks first at enabling technologies-- technologies that solve problems that are not Machine Translation proper but are linked closely to the development of a Machine Translation system. These include the acquisition of bilingual sentence-aligned data from comparable corpora, automatic construction of multilingual name dictionaries, and word alignment. The book then presents new or improved statistical Machine Translation techniques, including a discriminative training framework for leveraging syntactic information, the use of semi-supervised and kernel-based learning methods, and the combination of multiple Machine Translation outputs in order to improve overall translation quality.ContributorsSrinivas Bangalore, Nicola Cancedda, Josep M. Crego, Marc Dymetman, Jakob Elming, George Foster, Jesús Giménez, Cyril Goutte, Nizar Habash, Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak, Alexandre Klementiev, Gregor Leusch, Pierre Mahé, Lluís Màrquez, Evgeny Matusov, I. Dan Melamed, Ion Muslea, Hermann Ney, Bruno Pouliquen, Dan Roth, Anoop Sarkar, John Shawe-Taylor, Ralf Steinberger, Joseph Turian, Nicola Ueffing, Masao Utiyama, Zhuoran Wang, Benjamin Wellington, Kenji Yamada

  • Discriminant Analysis for Dimensionality Reduction: An Overview of Recent Developments

    This chapter contains sections titled: Introduction Overview of Linear Discriminant Analysis A Unified Framework for Generalized LDA A Least Squares Formulation for LDA Semisupervised LDA Extensions to Kernel-Induced Feature Space Other LDA Extensions Conclusion References

  • The Discretization Process: Basis/Testing Functions and Convergence

    This chapter contains sections titled: Inner Product Space The Method of Moments Examples of Subsectional Basis Functions Interpolation Error Dispersion Analysis Differentiability Constraints on Basis and Testing Functions Eigenvalue Projection Theory Classification of Operators for Several Canonical Equations Convergence Arguments Based on Galerkin's Method Convergence Arguments Based on Degenerate Kernel Analogs Convergence Arguments Based on Projection Operators The Stationary Character of Functionals Evaluated Using Numerical Solutions Summary This chapter contains sections titled: References Problems

  • Low-Density Separation

    In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi- Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low- density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tÿbingen. Schölkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large- Margin Classifiers (2000), and Kernel Methods in Computational B iology (2004), all published by The MIT Press.</P

  • Comparative Metric Semantics for Commit in Or-Parallel Logic Programming

    For the control flow kernel of or-parallel Prolog with commit an operational and a denotational model are constructed and related using techniques from metric semantics. By maintaining explicit scope information a compositional handling of the commit for the denotational model is established. By application of an abstraction function, which deletes this extra information, the operational semantics is recovered.



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