Conferences related to Support vector machines

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (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 full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,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 16th International Workshop on Advanced Motion Control (AMC)

AMC2020 is the 16th in a series of biennial international workshops on Advanced Motion Control which aims to bring together researchers from both academia and industry and to promote omnipresent motion control technologies and applications.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.



Periodicals related to Support vector machines

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Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on

Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.


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


Dielectrics and Electrical Insulation, IEEE Transactions on

Electrical insulation common to the design and construction of components and equipment for use in electric and electronic circuits and distribution systems at all frequencies.



Most published Xplore authors for Support vector machines

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Xplore Articles related to Support vector machines

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Tutorials

2018 IEEE International Test Conference in Asia (ITC-Asia), 2018

Provides an abstract for each of the tutorial presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.


A Control Method for Power System Stabilizers by means of a Support Vector Machine

2011 46th International Universities' Power Engineering Conference (UPEC), 2011

This paper proposes a new control mechanism for power system stabilizer (PSS) by means of Support Vector Machine (SVM). Plural PSS settings were suitably determined in advance and would be selected by SVM mechanism according to system states to improve overall performance. Learning process is very important and the learning method is also proposed in this paper. The proposed method ...


Analysis of wideband forward looking synthetic aperture radar for sensing land mines

Radio Science, 2004

Signal processing algorithms are considered for the analysis of wideband, forward looking synthetic aperture radar data and for sensing metal and plastic land mines, with principal application to unpaved roads. Simple prescreening algorithms are considered for reduction of the search space required for a subsequent classifier. The classifier employs features based on viewing the target at multiple ranges, with classification ...


Danger Feature-Based Negative Selection Algorithm

Artificial Immune System: Applications in Computer Security, None

This chapter presents a danger feature-based negative selection algorithm (DFNSA). In the DFNSA, the danger feature space is divided into four parts, and the information of danger features is reserved as much as possible, laying a good foundation for measuring the danger of a sample. A danger feature is a feature with dangerous properties, that is able to identify its ...


HRRP Classification by Using Improved SVM Decision Tree

2006 6th World Congress on Intelligent Control and Automation, 2006

Support vector machine (SVM) has been used in high resolution range profile (HRRP) classification for its good generalization ability for the pattern classification problem with high feature dimension and small training set. In order to perform multi-class classification, decision-tree-based SVM was studied, the structure and the classification performance of the SVM decision tree was analyzed. A separability measure which based ...



Educational Resources on Support vector machines

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IEEE.tv Videos

Vladimir Vapnik accepts the IEEE John Von Neumann Medal - Honors Ceremony 2017
Learning through Deterministic Assignment of Hidden Parameter
IMS 2011 Microapps - Vector-Receiver Load Pull - Measurement Accuracy at its Best
IMS 2011 Microapps - Techniques for Validating a Vector Network Analyzer Calibration When Using Microwave Probes
IMS 2011 Microapps - IQ Mixer Measurements: Techniques for Complete Characterization of IQ Mixers Using a Multi-Port Vector Network Analyzer
IMS 2012 Microapps - Passive Intermodulation (PIM) measurement using vector network analyzer Osamu Kusano, Agilent CTD-Kobe
IMS 2012 Microapps - Basic Amplifier Measurements with the RF Vector Network Analyzer (VNA) Taku Hirato, Agilent
Robots, Politics, and Ethics: How Autonomous Driving Transforms Our Way of Thinking About Machines
Micro-Apps 2013: Alternative Methods and Optimization Techniques for Vector Modulation
MicroApps: 200W RF Power Amplifer Design using a Nonlinear Vector Network Analyzer and Measured Load-Dependent X-Parameters (2) (Agilent Technologies)
From Mimicry to Mastery: Creating Machines that Augment Human Skill
SIMD Programming in VOLK, the Vector-Optimized Library of Kernels
State-of-the-art Electrical Machines for Hybrid Electric Vehicles
Demonstration of a Coherent Tunable Amplifier for All-Optical Ising Machines: IEEE Rebooting Computing 2017
Signal Processing and Machine Learning
IEEE ISEC Keynote Session - Complete Live-stream Recording
Qing-Chang Zhong, Distinguished Lecturer - PELS
Superconducting RF Cavities and Future Particle Accelerators - Applied Superconductivity Conference 2018
Randomization-Based Deep & Shallow Neural Networks
Self-Organization with Information Theoretic Learning

IEEE-USA E-Books

  • Tutorials

    Provides an abstract for each of the tutorial presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.

  • A Control Method for Power System Stabilizers by means of a Support Vector Machine

    This paper proposes a new control mechanism for power system stabilizer (PSS) by means of Support Vector Machine (SVM). Plural PSS settings were suitably determined in advance and would be selected by SVM mechanism according to system states to improve overall performance. Learning process is very important and the learning method is also proposed in this paper. The proposed method has been applied, and the obtained PSS control system was tested on an example power system (IEEJ WEST10). Compared with the previous results, the proposed method shows better performance even if in severe cases.

  • Analysis of wideband forward looking synthetic aperture radar for sensing land mines

    Signal processing algorithms are considered for the analysis of wideband, forward looking synthetic aperture radar data and for sensing metal and plastic land mines, with principal application to unpaved roads. Simple prescreening algorithms are considered for reduction of the search space required for a subsequent classifier. The classifier employs features based on viewing the target at multiple ranges, with classification implemented via a support vector machine and a relevance vector machine (RVM). Concerning classifier training, we consider cases for which training is performed on both mine and nonmine (clutter) data. In addition, motivated by the fact that the clutter statistics may vary significantly between the training and testing data, we also consider RVM implementation when we only train on mine data.

  • Danger Feature-Based Negative Selection Algorithm

    This chapter presents a danger feature-based negative selection algorithm (DFNSA). In the DFNSA, the danger feature space is divided into four parts, and the information of danger features is reserved as much as possible, laying a good foundation for measuring the danger of a sample. A danger feature is a feature with dangerous properties, that is able to identify its corresponding dangerous operations. It is the basic element for an immune system to decide whether an immune response should be produced. In order to incorporate the DFNSA into the procedure of malware detection, a DFNSA-based malware detection (DFNSA-MD) model is proposed. The danger of a sample is measured precisely in this way and used to classify the sample. Comprehensive experimental results suggest that the DFNSA is able to reserve as much information about the danger features as possible, and the DFNSA-MD model is effective to detect unseen malware.

  • HRRP Classification by Using Improved SVM Decision Tree

    Support vector machine (SVM) has been used in high resolution range profile (HRRP) classification for its good generalization ability for the pattern classification problem with high feature dimension and small training set. In order to perform multi-class classification, decision-tree-based SVM was studied, the structure and the classification performance of the SVM decision tree was analyzed. A separability measure which based on the distribution of the training samples was defined, the defined separability measure was applied into the formation of the decision tree, and an improved algorithm for SVM decision tree was proposed. The scheme of using the improved algorithm for SVM decision tree to classify HRRP was given. Experiments using the range profile datasets prove the effectiveness of our scheme

  • A note on the classification error of an SVM in one dimension

    There are many algorithms available for detecting noise corrupted signals in background clutter. In cases where the exact statistics of the noise and clutter are unknown, the optimal detector may be estimated from a set of samples of each. One method for doing this is the support vector machine (SVM), which has a detection performance that is dependent on some regularisation parameter C, and cannot be determined a-priori. The standard method of choosing C is by trying values and choosing the one which minimises the detection error on a cross-validation set. It is often assumed that as the size of the training set increases, the resulting discriminant will give the best possible detection rate on an independent test set. This paper investigates two simple 1D examples: uniform and normal distributions. An example is provided where the optimum detection rate cannot be achieved by an SVM regardless of the C chosen value.

  • A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers

    We present a small sphere and large margin approach for novelty detection problems, where the majority of training data are normal examples. In addition, the training data also contain a small number of abnormal examples or outliers. The basic idea is to construct a hypersphere that contains most of the normal examples, such that the volume of this sphere is as small as possible, while at the same time the margin between the surface of this sphere and the outlier training data is as large as possible. This can result in a closed and tight boundary around the normal data. To build such a sphere, we only need to solve a convex optimization problem that can be efficiently solved with the existing software packages for training nu-support vector machines. Experimental results are provided to validate the effectiveness of the proposed algorithm.

  • 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.

  • Evolving kernel functions for SVMs by genetic programming

    hybrid model for evolving support vector machine (SVM) kernel functions is developed in this paper. The kernel expression is considered as a parameter of the SVM algorithm and the current approach tries to find the best expression for this SVM parameter. The model is a hybrid technique that combines a genetic programming (GP) algorithm and a support vector machine (SVM) algorithm. Each GP chromosome is a tree encoding the mathematical expression for the kernel function. The evolved kernel is compared to several human- designed kernels and to a previous genetic kernel on several datasets. Numerical experiments show that the SVM embedding our evolved kernel performs statistically better than standard kernels, but also than previous genetic kernel for all considered classification problems.

  • Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines

    Large-margin methods, such as support vector machines (SVMs), have been very successful in classification problems. Recently, maximum margin discriminant analysis (MMDA) was proposed that extends the large-margin idea to feature extraction. It often outperforms traditional methods such as kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFD). However, as in the SVM, its time complexity is cubic in the number of training points m, and is thus computationally inefficient on massive data sets. In this paper, we propose an (1 + isin)<sup>2</sup>-approximation algorithm for obtaining the MMDA features by extending the core vector machine. The resultant time complexity is only linear in m, while its space complexity is independent of m. Extensive comparisons with the original MMDA, KPCA, and KFD on a number of large data sets show that the proposed feature extractor can improve classification accuracy, and is also faster than these kernel-based methods by over an order of magnitude.



Standards related to Support vector machines

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No standards are currently tagged "Support vector machines"