Cybernetics

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Cybernetics is the interdisciplinary study of the structure of regulatory systems. (Wikipedia.org)






Conferences related to Cybernetics

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2014 IEEE International Conference on Systems, Man and Cybernetics - SMC

SMC2014 targets advances in Systems Science and Engineering, Human-Machine Systems, and Cybernetics involving state-of-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies, and emerging applications.


2013 11th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering)

The 2013 International Conference on ICT and Knowledge Engineering provides an international forum that brings together those actively involved in areas of interest to the ICT/Knowledge Engineering Systems and other related fields, to report on up-to-the-minute innovations and developments, to summarize the state-of-the-art, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics.

  • 2011 9th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2011) - Conference postponed to 2012

    The international conference has been held annually since 2003 with the cooperative effort for a number of organizations and universities in Thailand and other countries. It is aimed to promote research in ICT and Knowledge Engineering by bringing together researchers and practitioners in the field to exchange ideas, to report on up-to-the-minute innovations and developments, and to present results from research on ICT and Knowledge Engineering areas and various related fields.

  • 2012 10th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2012)

    The 10th International Conference on IEEE ICT and Knowledge Engineering 2012, jointly organized by Asia and Pacific Distance Multimedia Education Network (APDMEN), Siam University, and IEEE Thailand Section. The 2012 International Conference on ICT and Knowledge Engineering provides an international forum that brings together those actively involved in areas of interest to the ICT/Knowledge Engineering Systems and other related fields, to report on up-to-the-minute innovations and developments, to summarize the state-of-the-art, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics.

  • 2010 8th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2010)

    The 2010 International Conference on ICT and Knowledge Engineering provides an international forum that brings together those actively involved in areas of interest to the ICT/Knowledge Engineering Systems and other related fields, to report on up-to-the-minute innovations and developments, to summarize the state-of-the-art, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics.

  • 2009 7th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2009)

    The 2009 International Conference on ICT and Knowledge Engineering provides an international forum that brings together those actively involved in areas of interest to the ICT and Knowledge Engineering Systems, to report on up-to-the-minute innovations and developments, to summarize the state-of-the-art, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics.


2013 6th International Conference on Robotics, Automation and Mechatronics (RAM)

The goal of the RAM 2013 is to bring together experts from the field of robotics, automation and mechatronics to discuss on the state-of-the-art and to present new research findings and perspectives of future developments.

  • 2011 IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM)

    The goal of the RAM 2011 is to bring together experts from the field of robotics, automation and mechatronics to discuss on the state-of-the-art and to present new research findings and perspectives of future developments with respect to the conference themes. The RAM 2011 is held in conjunction with the IEEE International Conference on Cybernetics and Intelligent Systems (CIS 2011)

  • 2008 IEEE Conference on Robotics, Automation and Mechatronics (RAM)

    The CIS-RAM 2008 is to bring together experts from the field of cybernetics, intelligent systems robotics, automation, and mechatronics to present and discuss new research findings and perspectives of future developments with respect to the conference themes.


2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

HRI is a single -track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single-track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics.

  • 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    TOPICS: Robot companions, Lifelike robots, Assistive (health & personal care) robotics, Remote robots, Mixed initiative interaction, Multi-modal interaction, Long-term interaction with robots, Awareness and monitoring of humans, Task allocation and coordination, Autonomy and trust, Robot-team learning, User studies of HRI, Experiments on HRI collaboration, Ethnography and field studies, HRI software architectures

  • 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    * Robot companions * Lifelike robots * Assistive (health & personal care) robotics * Remote robots * Mixed initiative interaction * Multi-modal interaction * Long-term interaction with robots * Awareness and monitoring of humans * Task allocation and coordination * Autonomy and trust * Robot-team learning * User studies of HRI * Experiments on HRI collaboration * Ethnography and field studies * HRI software architectures


2013 IEEE 10th International Conference on Networking, Sensing and Control (ICNSC)

The main theme of the conference is Technology for efficient green networks . It will provide a remarkable opportunity for the academic and industrial communities to address new challenges and share solutions, and discuss future research directions.

  • 2012 9th IEEE International Conference on Networking, Sensing and Control (ICNSC)

    This conference will provide a remarkable opportunity for the academic and industrial community to address new challenges and share solutions, and discuss future research directions in the area of intelligent transportation systems and networks as well other areas of networking, sensing and control. It will feature plenary speeches, industrial panel sessions, funding agency panel sessions, interactive sessions, and invited/special sessions. Contributions are expected from academia, industry, and government agencies.

  • 2011 IEEE International Conference on Networking, Sensing and Control (ICNSC)

    The main theme of the conference is Next Generation Infrastructures . Infrastructures are the backbone of the economy and society. Especially the network bound infrastructures operated by public utilities and network industries provide essential services that are enabling for almost every economic and social activity. The crucial role of the infrastructure networks for energy and water supply, transportation of people and goods, and provision of telecommunication and information services.

  • 2010 International Conference on Networking, Sensing and Control (ICNSC)

    Provide a remarkable opportunity for the academic and industrial community to address new challenges and share solutions, and discuss future research directions.

  • 2009 International Conference on Networking, Sensing and Control (ICNSC)

    The main theme of the conference is advanced technologies for safety and functional maintenance. The real challenge is to obtain advanced control technology for safety and management technology and to construct an information system to share information on safetytechnology and on investigated accidents.


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Periodicals related to Cybernetics

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Education, IEEE Transactions on

Educational methods, technology, and programs; history of technology; impact of evolving research on education.


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

Systems engineering, including efforts that involve issue formnaulations, issue analysis and modeling, and decision making and issue interpretation at any of the life-cycle phases associated with the definition, development, and implementation of large systems. It will also include efforts that relate to systems management, systems engineering processes and a variety of systems engineering methods such as optimization, modeling and simulation. ...


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, ...


Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on

Applications, review, and tutorial papers within the scope of the Systems, Man and Cybernetics Society. Currently, this covers: (1) Integration of the theories of communication, control cybernetics, stochastics, optimization and system structure towards the formulation of a general theory of systems; (2) Development of systems engineering technology including problem definition methods, modeling, and stimulation, methods of systems experimentation, human factors ...




Xplore Articles related to Cybernetics

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A Model Reduction Methord Based On Optimal Approximation To The Dominant Energy (multi-input Multi-output)

Hu Shousong; Hu Minghua Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754404.png)


Use Of Systems Analysis Techniques In Ocean Resources Development

M. G. Johnson Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754399.png)


Research of Image Automatic Clipping Methods in Air-Ground Data Link System

Lian Jing; Wang Ke; Feng Jian 2006 International Conference on Machine Learning and Cybernetics, 2006

In communications using air-ground data link, the data that comes from various airborne image sensors is getting huge while the bandwidth of the data link is relatively narrow. To improve image transmission efficiency in battle field condition, it is required that the images acquired by a (CCD) aerial camera using central projection were transmitted at high fidelity and speed over ...


Relative density based k-nearest neighbors clustering algorithm

Qing-Bao Liu; Su Deng; Chang-Hui Lu; Bo Wang; Yong-Feng Zhou Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2003

With strong ability of discovering arbitrary shape clusters and handling noise, density based clustering is one of primary methods for data mining. This paper provides a k-nearest neighbors clustering algorithm based on relative density, which efficiently resolves these problem of being very sensitive to the user-defined parameters and too difficult for users to determine the parameters.


Model Predictive Control of a Ball and Plate laboratory model

Matej Oravec; Anna Jadlovská 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2015

The papers presents an implementation of the predictive state space control algorithm, called Model Predictive Control (MPC). This control algorithm is verified on the Ball and Plate laboratory model, called B&P_KYB, for the reference trajectory tracking. The control algorithm is first verified using the derived nonlinear simulation model in Matlab/Simulink. Since simulation results are acceptable, an experiment is realized on ...


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Educational Resources on Cybernetics

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eLearning

A Model Reduction Methord Based On Optimal Approximation To The Dominant Energy (multi-input Multi-output)

Hu Shousong; Hu Minghua Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754404.png)


Use Of Systems Analysis Techniques In Ocean Resources Development

M. G. Johnson Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, 1988

First Page of the Article ![](/xploreAssets/images/absImages/00754399.png)


Research of Image Automatic Clipping Methods in Air-Ground Data Link System

Lian Jing; Wang Ke; Feng Jian 2006 International Conference on Machine Learning and Cybernetics, 2006

In communications using air-ground data link, the data that comes from various airborne image sensors is getting huge while the bandwidth of the data link is relatively narrow. To improve image transmission efficiency in battle field condition, it is required that the images acquired by a (CCD) aerial camera using central projection were transmitted at high fidelity and speed over ...


Relative density based k-nearest neighbors clustering algorithm

Qing-Bao Liu; Su Deng; Chang-Hui Lu; Bo Wang; Yong-Feng Zhou Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), 2003

With strong ability of discovering arbitrary shape clusters and handling noise, density based clustering is one of primary methods for data mining. This paper provides a k-nearest neighbors clustering algorithm based on relative density, which efficiently resolves these problem of being very sensitive to the user-defined parameters and too difficult for users to determine the parameters.


Model Predictive Control of a Ball and Plate laboratory model

Matej Oravec; Anna Jadlovská 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2015

The papers presents an implementation of the predictive state space control algorithm, called Model Predictive Control (MPC). This control algorithm is verified on the Ball and Plate laboratory model, called B&P_KYB, for the reference trajectory tracking. The control algorithm is first verified using the derived nonlinear simulation model in Matlab/Simulink. Since simulation results are acceptable, an experiment is realized on ...


More eLearning Resources

IEEE-USA E-Books

  • Author Index

    These sixty contributions from researchers in ethology, ecology, cybernetics, artificial intelligence, robotics, and related fields delve into the behaviors and underlying mechanisms that allow animals and, potentially, robots to adapt and survive in uncertain environments. They focus in particular on simulation models in order to help characterize and compare various organizational principles or architectures capable of inducing adaptive behavior in real or artificial animals.Jean-Arcady Meyer is Director of Research at CNRS, Paris. Stewart W. Wilson is a Scientist at The Rowland Institute for Science, Cambridge, Massachusetts.

  • 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

  • References

    Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field. Contributors Yasemin Altun, Gï¿¿ï¿¿khan Bakir [no dot over i], Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daumï¿¿ï¿¿ III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Pï¿¿ï¿¿rez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Schï¿¿ï¿¿lkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston Gï¿¿ï¿¿khan Bakir [no dot over i] is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tï¿¿ï¿¿bingen, Germany. Thomas Hofmann is a Director of Engineering at Google's Engineering Center in Zurich and Adjunct Associate Professor of Computer Science at Brown University. Bernhard Schï¿¿ï¿¿lkopf is Director of the Max Planck Institute for Biological Cybernetics and Professor at the Technical University Berlin. Alexander J. Smola is Senior Principal Researcher and Mac hine Learning Program Leader at National ICT Australia/Australian National University, Canberra. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania. S. V. N. Vishwanathan is Senior Researcher in the Statistical Machine Learning Program, National ICT Australia with an adjunct appointment at the Research School for Information Sciences and Engineering, Australian National University.

  • A Discussion of Semi-Supervised Learning and Transduction

    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

  • The Ratio Club: A Hub of British Cybernetics

    This chapter contains sections titled: The Members, Genesis of the Club, The Way Forward, Club Meetings, Themes, Interdisciplinarity, The Legacy of the Club, Acknowledgments, References

  • Presentation of a MazeSolving MachineTransactions 8th Cybernetics Conference, Josiah Macy Jr. Foundation, 1952.

    This important book, the first published collection of papers by Claude E. Shannon, is a fascinating guide to all of the published articles from this world-renowned inventor, tinkerer, puzzle-solver, prankster, and father of information theory. Includes his seminal article THE MATHEMATICAL THEORY OF COMMUNICATION.

  • Contributors

    Machine learning develops intelligent computer systems that are able to generalize from previously seen examples. A new domain of machine learning, in which the prediction must satisfy the additional constraints found in structured data, poses one of machine learning's greatest challenges: learning functional dependencies between arbitrary input and output domains. This volume presents and analyzes the state of the art in machine learning algorithms and theory in this novel field. The contributors discuss applications as diverse as machine translation, document markup, computational biology, and information extraction, among others, providing a timely overview of an exciting field. Contributors Yasemin Altun, Gï¿¿ï¿¿khan Bakir [no dot over i], Olivier Bousquet, Sumit Chopra, Corinna Cortes, Hal Daumï¿¿ï¿¿ III, Ofer Dekel, Zoubin Ghahramani, Raia Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann, Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford Noble, Fernando Pï¿¿ï¿¿rez-Cruz, Massimiliano Pontil, Marc'Aurelio Ranzato, Juho Rousu, Craig Saunders, Bernhard Schï¿¿ï¿¿lkopf, Matthias W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer, Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis Tsochantaridis, S.V.N Vishwanathan, Jason Weston Gï¿¿ï¿¿khan Bakir [no dot over i] is Research Scientist at the Max Planck Institute for Biological Cybernetics in Tï¿¿ï¿¿bingen, Germany. Thomas Hofmann is a Director of Engineering at Google's Engineering Center in Zurich and Adjunct Associate Professor of Computer Science at Brown University. Bernhard Schï¿¿ï¿¿lkopf is Director of the Max Planck Institute for Biological Cybernetics and Professor at the Technical University Berlin. Alexander J. Smola is Senior Principal Researcher and Mac hine Learning Program Leader at National ICT Australia/Australian National University, Canberra. Ben Taskar is Assistant Professor in the Computer and Information Science Department at the University of Pennsylvania. S. V. N. Vishwanathan is Senior Researcher in the Statistical Machine Learning Program, National ICT Australia with an adjunct appointment at the Research School for Information Sciences and Engineering, Australian National University.

  • Architectures, Organizational Principles, and Functional Approaches

    These sixty contributions from researchers in ethology, ecology, cybernetics, artificial intelligence, robotics, and related fields delve into the behaviors and underlying mechanisms that allow animals and, potentially, robots to adapt and survive in uncertain environments. They focus in particular on simulation models in order to help characterize and compare various organizational principles or architectures capable of inducing adaptive behavior in real or artificial animals.Jean-Arcady Meyer is Director of Research at CNRS, Paris. Stewart W. Wilson is a Scientist at The Rowland Institute for Science, Cambridge, Massachusetts.

  • Motivation and Emotion

    These sixty contributions from researchers in ethology, ecology, cybernetics, artificial intelligence, robotics, and related fields delve into the behaviors and underlying mechanisms that allow animals and, potentially, robots to adapt and survive in uncertain environments. They focus in particular on simulation models in order to help characterize and compare various organizational principles or architectures capable of inducing adaptive behavior in real or artificial animals.Jean-Arcady Meyer is Director of Research at CNRS, Paris. Stewart W. Wilson is a Scientist at The Rowland Institute for Science, Cambridge, Massachusetts.

  • References

    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



Standards related to Cybernetics

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No standards are currently tagged "Cybernetics"