Conferences related to Machine Learning

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2014 IEEE Conference on Computational Intelligence and Games (CIG)

Games can be used as a challenging scenery for benchmarking methods from computational intelligencesince they provide dynamic and competitive elements that are germane to real-world problems. This conference brings together leading researchers andpractitioners from academia and industry to discuss recent advances and explore future directions in this field.

  • 2012 IEEE Conference on Computational Intelligence and Games (CIG)

    Games provide dynamic environments modelling many real-world problems and methods from computational intelligence promise to having a big impact on game technology and development. CIG 2012 brings together leading researchers, designers, developers, and practitioners from academia and industry to discuss recent advances and explore future directions in this ever changing field.

  • 2011 IEEE Conference on Computational Intelligence and Games (CIG)

    Games have proven to be an ideal domain for the study of computational intelligence as not only are they fun to play and interesting to observe, but they provide competitive and dynamic environments that model many real-world problems. The 2010 IEEE Symposium on Computational Intelligence and Games brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in this field.

  • 2010 IEEE Symposium on Computational Intelligence and Games (CIG)

    Games have proven to be an ideal domain for the study of computational intelligence as not only are they fun to play and interesting to observe, but they provide competitive and dynamic environments that model many real-world problems. The 2010 IEEE Symposium on Computational Intelligence and Games brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in this field.

  • 2009 IEEE Symposium on Computational Intelligence and Games (CIG)

    The 2009 IEEE Symposium on Computational Intelligence and Games brings together leading researchers and practitioners from academia and industry to discuss recent advances and explore future directions in the area of computational intelligence applied to games.


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 IEEE International Conference on Systems, Man and Cybernetics - SMC

    SMC 2013 targets advances in Systems Science and Engineering Human-machine Systems and Cybernetics involving state-of-the-art technologies interacting with humans to provide an enriching experience and thereby improving the quality of lives including theories, methodologies and emerging applications.

  • 2012 IEEE International Conference on Systems, Man and Cybernetics - SMC

    Theory, research and technology advances including applications in all aspects of systems science and engineering, human machine systems, and emerging cybernetics.

  • 2011 IEEE International Conference on Systems, Man and Cybernetics - SMC

    Theory, research, and technology advances including applications in all aspects of systems science and engineering, human machine systems, and emerging cybernetics.

  • 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC

    The 2010 IEEE International Conference on Systems, Man, and Cybernetics (SMC2010) provides an international forum that brings together those actively involved in areas of interest to the IEEE Systems, Man, and Cybernetics Society, 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 IEEE International Conference on Systems, Man and Cybernetics - SMC

    The 2009 IEEE International Conference on Systems, Man, and Cybernetics (SMC2009) provides an international forum that brings together those actively involved in areas of interest to the IEEE Systems, Man, and Cybernetics Society, 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 10th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

AVSS focuses on video and signal based surveillance. Topics include: 1) Sensors and data fusion, 2) Processing, detection & recognition, 3) Analytics, behavior & biometrics, 4) Data management and human-computer interfaces, 5) Applications and 6) Privacy Issues


2013 12th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

Cognitive Informatics (CI) is a cutting-edge and multidisciplinary research field that tackles the fundamental problems shared by modern informatics, computing, AI, cybernetics, computational intelligence, cognitive science, intelligence science, neuropsychology, brain science, systems science, software engineering, knowledge engineering, cognitive robots, scientific philosophy, cognitive linguistics, life sciences, and cognitive computing.

  • 2012 11th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive informatics and Cognitive Computing are a transdisciplinary enquiry on the internal information processing mechanisms and processes of the brain and their engineering applications in cognitive computers, computational intelligence, cognitive robots, cognitive systems, and in the AI, IT, and software industries. The 11th IEEE Int l Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 12) focuses on the theme of e-Brain and Cognitive Computers.

  • 2011 10th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

    Cognitive Informatics and Cognitive Computing are a transdisciplinary enquiry on the internal information processing mechanisms and processes of the brain and their engineering applications in cognitive computers, computational intelligence, cognitive robots, cognitive systems, and in the AI, IT, and software industries. The 10th IEEE Int l Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 11) focuses on the theme of Cognitive Computers and the e-Brain.

  • 2010 9th IEEE International Conference on Cognitive Informatics (ICCI)

    Cognitive Informatics (CI) is a cutting-edge and transdisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, AI, cybernetics, computational intelligence, cognitive science, neuropsychology, medical science, systems science, software engineering, telecommunications, knowledge engineering, philosophy, linguistics, economics, management science, and life sciences.

  • 2009 8th IEEE International Conference on Cognitive Informatics (ICCI)

    The 8th IEEE International Conference on Cognitive Informatics (ICCI 09) focuses on the theme of Cognitive Computing and Semantic Mining. The objectives of ICCI'09 are to draw attention of researchers, practitioners, and graduate students to the investigation of cognitive mechanisms and processes of human information processing, and to stimulate the international effort on cognitive informatics research and engineering applications.


2013 16th International Conference on Information Fusion - (FUSION 2013)

Scope of the conference is to provide medium to discuss advances and applications of fusion methodologies. Conference will include contributions in the areas of fusion methodologies, theory and representation, algorithms and modelling and simulation.

  • 2012 15th International Conference on Information Fusion (FUSION)

    The objective of the conference is to provide a forum to discuss advances and applications of fusion methodologies. The conference will feature keynote speeches, special sessions, and tutorials on topics of current interest.

  • 2011 International Conference on Information Fusion (FUSION)

    This conference is dedicated to advancing the knowledge, theory, and applications of information fusion. Topics will include radar processing, artificial intelligence, target tracking, classification, sensor networks, and sensor management.

  • 2010 13th International Conference on Information Fusion - (FUSION 2010)

    This annual conference aims to bring together professionals from around the world to facilitate discussion on the recent advances and pertinent issues in fusion technologies. Key themes are Methodologies, Algorithmic Domains, Solution Paradigms, Sensor Specific Processing and Fusion, Modelling, Simulation and Evaluation and Application Domains.

  • 2009 12th International Conference on Information Fusion - (FUSION 2009)

    Overview -- The 12th International Conference on Information Fusion will be held in Seattle, Washington, at the Grand Hyatt Seattle Hotel. Authors are invited to submit papers describing advances and applications in information fusion, with submission of non-traditional topics encouraged. Conference Site -- Pacific Northwest is one of the most scenic parts of United States and Seattle is the home of some of the world's biggest technology companies such as Boeing and Microsoft. Seattle is easily accessible

  • 2008 11th International Conference on Information Fusion - (FUSION 2008)

    The conference exists to advance the understanding of information fusion methodologies, algorithms, technologies and applications.

  • 2007 10th International Conference on Information Fusion - (FUSION 2007)

    This conference is the annual conference of the International Society of Information Fusion (ISIF:www.isif.org). It is the forum of scientists and engineers involved in sensor fusion, data fusion, information fusion and knowledge management.

  • 2006 9th International Conference on Information Fusion - (FUSION 2006)


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Periodicals related to Machine Learning

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Consumer Electronics, IEEE Transactions on

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes


Evolutionary Computation, IEEE Transactions on

Papers on application, design, and theory of evolutionary computation, with emphasis given to engineering systems and scientific applications. Evolutionary optimization, machine learning, intelligent systems design, image processing and machine vision, pattern recognition, evolutionary neurocomputing, evolutionary fuzzy systems, applications in biomedicine and biochemistry, robotics and control, mathematical modelling, civil, chemical, aeronautical, and industrial engineering applications.


Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


Knowledge and Data Engineering, IEEE Transactions on

Artificial intelligence techniques, including speech, voice, graphics, images, and documents; knowledge and data engineering tools and techniques; parallel and distributed processing; real-time distributed processing; system architectures, integration, and modeling; database design, modeling, and management; query design, and implementation languages; distributed database control; statistical databases; algorithms for data and knowledge management; performance evaluation of algorithms and systems; data communications aspects; system ...


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Xplore Articles related to Machine Learning

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Dimensionality reduction by rank preservation

Victor Onclinx; John A. Lee; Vincent Wertz; Michel Verleysen The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces. To be faithful and reliable, the representation is usually required to preserve proximity relationships. In practice, methods like multidimensional scaling try to fulfill this requirement by preserving pairwise distances in the low-dimensional representation. However, such a simplification does not easily allow for local scalings in the representation. It also ...


Early network failure detection system by analyzing Twitter data

Kei Takeshita; Masahiro Yokota; Ken Nishimatsu 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015

Mobile network failures have occurred many times in recent years. Some network failures become "silent" failures that mobile carriers cannot detect because of incomplete rules concerning failure detection by the network operating system. However, the increasing number of services and devices, and the increasing complexity of the network make it hard to generate rules that cover all network failures. Therefore, ...


Combining Simulation and Machine Learning to Recognize Function in 4D

Russ Biagio Altman 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

This paper is a talk by Russ Biagio Altman. It discusses structure-based protein function annotation using machine learning, physics-based simulation of structure, and how they can be profitably combined to improve our understanding of molecular structure and function.


Toward a No-Reference Image Quality Assessment Using Statistics of Perceptual Color Descriptors

Dohyoung Lee; Konstantinos N. Plataniotis IEEE Transactions on Image Processing, 2016

Analysis of the statistical properties of natural images has played a vital role in the design of no-reference (NR) image quality assessment (IQA) techniques. In this paper, we propose parametric models describing the general characteristics of chromatic data in natural images. They provide informative cues for quantifying visual discomfort caused by the presence of chromatic image distortions. The established models ...


Multiple Classifiers Based Incremental Learning Algorithm for Learning in Nonstationary Environments

Michael D. Muhlbaier; Robi Polikar 2007 International Conference on Machine Learning and Cybernetics, 2007

We describe an incremental learning algorithm designed to learn in challenging non-stationary environments, where the underlying data distribution that governs the classification problem changes at an unknown rate. The algorithm is based on a multiple classifier system that generates a new classifier every time a new dataset becomes available from the changing environment. We consider the particularly challenging form of ...


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Educational Resources on Machine Learning

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eLearning

Dimensionality reduction by rank preservation

Victor Onclinx; John A. Lee; Vincent Wertz; Michel Verleysen The 2010 International Joint Conference on Neural Networks (IJCNN), 2010

Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces. To be faithful and reliable, the representation is usually required to preserve proximity relationships. In practice, methods like multidimensional scaling try to fulfill this requirement by preserving pairwise distances in the low-dimensional representation. However, such a simplification does not easily allow for local scalings in the representation. It also ...


Early network failure detection system by analyzing Twitter data

Kei Takeshita; Masahiro Yokota; Ken Nishimatsu 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), 2015

Mobile network failures have occurred many times in recent years. Some network failures become "silent" failures that mobile carriers cannot detect because of incomplete rules concerning failure detection by the network operating system. However, the increasing number of services and devices, and the increasing complexity of the network make it hard to generate rules that cover all network failures. Therefore, ...


Combining Simulation and Machine Learning to Recognize Function in 4D

Russ Biagio Altman 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

This paper is a talk by Russ Biagio Altman. It discusses structure-based protein function annotation using machine learning, physics-based simulation of structure, and how they can be profitably combined to improve our understanding of molecular structure and function.


Toward a No-Reference Image Quality Assessment Using Statistics of Perceptual Color Descriptors

Dohyoung Lee; Konstantinos N. Plataniotis IEEE Transactions on Image Processing, 2016

Analysis of the statistical properties of natural images has played a vital role in the design of no-reference (NR) image quality assessment (IQA) techniques. In this paper, we propose parametric models describing the general characteristics of chromatic data in natural images. They provide informative cues for quantifying visual discomfort caused by the presence of chromatic image distortions. The established models ...


Multiple Classifiers Based Incremental Learning Algorithm for Learning in Nonstationary Environments

Michael D. Muhlbaier; Robi Polikar 2007 International Conference on Machine Learning and Cybernetics, 2007

We describe an incremental learning algorithm designed to learn in challenging non-stationary environments, where the underlying data distribution that governs the classification problem changes at an unknown rate. The algorithm is based on a multiple classifier system that generates a new classifier every time a new dataset becomes available from the changing environment. We consider the particularly challenging form of ...


More eLearning Resources

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IEEE-USA E-Books

  • Kernel Machines

    This chapter contains sections titled: 13.1 Introduction, 13.2 Optimal Separating Hyperplane, 13.3 The Nonseparable Case: Soft Margin Hyperplane, 13.4 ??-SVM, 13.5 Kernel Trick, 13.6 Vectorial Kernels, 13.7 Defining Kernels, 13.8 Multiple Kernel Learning, 13.9 Multiclass Kernel Machines, 13.10 Kernel Machines for Regression, 13.11 Kernel Machines for Ranking, 13.12 One-Class Kernel Machines, 13.13 Large Margin Nearest Neighbor Classifier, 13.14 Kernel Dimensionality Reduction, 13.14 Kernel Dimensionality Reduction, 13.16 Exercises, 13.17 References

  • Graphical Models

    This chapter contains sections titled: 14.1 Introduction, 14.2 Canonical Cases for Conditional Independence, 14.3 Generative Models, 14.4 d-Separation, 14.5 Belief Propagation, 14.6 Undirected Graphs: Markov Random Fields, 14.7 Learning the Structure of a Graphical Model, 14.8 Influence Diagrams, 14.9 Notes, 14.10 Exercises, 14.11 References

  • Index

    In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well- founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

  • Appendix B: Markov Processes

    This appendix contains sections titled: Markov Processes Semi-Markov Process

  • No title

    Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, m ssage-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

  • Schemata

    Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learning techniques in the design of complex devices such as aircraft turbines and integrated circuits. Adaptation in Natural and Artificial Systems is the book that initiated this field of study, presenting the theoretical foundations and exploring applications.In its most familiar form, adaptation is a biological process, whereby organisms evolve by rearranging genetic material to survive in environments confronting them. In this now classic work, Holland presents a mathematical model that allows for the nonlinearity of such complex interactions. He demonstrates the model's universality by applying it to economics, physiological psychology, game theory, and artificial intelligence and then outlines the way in which this approach modifies the traditional views of mathematical genetics.Initially applying his concepts to simply defined artificial systems with limited numbers of parameters, Holland goes on to explore their use in the study of a wide range of complex, naturally occuring processes, concentrating on systems having multiple factors that interact in nonlinear ways. Along the way he accounts for major effects of coadaptation and coevolution: the emergence of building blocks, or schemata, that are recombined and passed on to succeeding generations to provide, innovations and improvements.John H. Holland is Professor of Psychology and Professor of Electrical Engineering and Computer Science at the University of Michigan. He is also Maxwell Professor at the Santa Fe Institute and is Director of the University of Michigan/Santa Fe Institute Advanced Research Program.

  • Importance Estimation

    This chapter contains sections titled: Kernel Density Estimation, Kernel Mean Matching, Logistic Regression, Kullback-Leibler Importance Estimation Procedure, Least-Squares Importance Fitting, Unconstrained Least-Squares Importance Fitting, Numerical Examples, Experimental Comparison, Summary

  • Hidden Markov Models

    This chapter contains sections titled: 15.1 Introduction, 15.2 Discrete Markov Processes, 15.3 Hidden Markov Models, 15.4 Three Basic Problems of HMMs, 15.5 Evaluation Problem, 15.6 Finding the State Sequence, 15.7 Learning Model Parameters, 15.8 Continuous Observations, 15.9 The HMM as a Graphical Model, 15.10 Model Selection in HMMs, 15.11 Notes, 15.12 Exercises, 15.13 References

  • Notation and Symbols

    In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well- founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

  • Front Matter

    This chapter contains sections titled: Half Title, Adaptive Computation and Machine Learning, Title, Copyright, Dedication, Contents, Series Foreword, Preface



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