Conferences related to Support vector machine classification

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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.


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.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

Neural Engineering

  • 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)

    Neural Engineering is an emerging core discipline,which coalesces neuroscience with engineering.Members of both the Neuroscience and Engineering Communities areencouraged to attend this highly multidisciplinarymeeting. The conference will highlight the emergingengineering innovations in the restoration andenhancement of impaired sensory, motor, andcognitive functions, novel engineering for deepeningknowledge of brain function, and advanced designand use of neurotechnologies

  • 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

    Neural engineering deals with many aspects of basic and clinical problemsassociated with neural dysfunction including the representation of sensory and motor information, theelectrical stimulation of the neuromuscular system to control the muscle activation and movement, theanalysis and visualization of complex neural systems at multi -scale from the single -cell and to the systemlevels to understand the underlying mechanisms, the development of novel neural prostheses, implantsand wearable devices to restore and enhance the impaired sensory and motor systems and functions.

  • 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)

    Neural engineering deals with many aspects of basic and clinical problems associated with neural dysfunction including the representation of sensory and motor information, the electrical stimulation of the neuromuscular system to control the muscle activation and movement, the analysis and visualization of complex neural systems at multi-scale from the single-cell and to the system levels to understand the underlying mechanisms, the development of novel neural prostheses, implants and wearable devices to restore and enhance the impaired sensory and motor systems and functions.

  • 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER)

    highlight the emerging field, Neural Engineering that unites engineering, physics, chemistry, mathematics, computer science with molecular, cellular, cognitive and behavioral neuroscience and encompasses such areas as replacing or restoring lost sensory and motor abilities, defining the organizing principles and underlying mechanisms of neural systems, neurorobotics, neuroelectronics, brain imaging and mapping, cognitive science and neuroscience.

  • 2009 4th International IEEE/EMBS Conference on Neural Engineering (NER)

    highlight the emerging field, Neural Engineering that unites engineering, physics, chemistry, mathematics, computer science with molecular, cellular, cognitive and behavioral neuroscience and encompasses such areas as replacing or restoring lost sensory and motor abilities, defining the organizing principles and underlying mechanisms of neural systems, neurorobotics, neuroelectronics, brain imaging and mapping, cognitive science and neuroscience.

  • 2007 3rd International IEEE/EMBS Conference on Neural Engineering

  • 2005 2nd International IEEE/EMBS Conference on Neural Engineering

  • 2003 1st International IEEE/EMBS Conference on Neural Engineering


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Periodicals related to Support vector machine classification

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Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


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 for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Most published Xplore authors for Support vector machine classification

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Xplore Articles related to Support vector machine classification

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Vector power in alternating-current circuits

Proceedings of the American Institute of Electrical Engineers, 1910

It has long been known that in any simple alternating-current circuit, the current and voltage may be conveniently regarded as rotatable vector quantities.1It is also known that the power in such circuits is not to be regarded as the vector product of the rotating vector voltage and rotating vector current.2It does not seem to have been pointed out, however, that, ...


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 ...


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

Final Program and Abstracts on Information, Decision and Control, 2002

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 ...


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

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009

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 ...


High speed networks that preserve continuity and accuracy

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

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


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Educational Resources on Support vector machine classification

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IEEE.tv Videos

Vladimir Vapnik accepts the IEEE John Von Neumann Medal - Honors Ceremony 2017
Solving Sparse Representation for Image Classification using Quantum D-Wave 2X Machine - IEEE Rebooting Computing 2017
Learning through Deterministic Assignment of Hidden Parameter
Deeper Neural Networks - Kurt Keutzer - LPIRC 2019
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
Hardware-Software Co-Design for an Analog-Digital Accelerator for Machine Learning - Dejan Milojicic - ICRC 2018
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
Computing Based on Material Training: Application to Binary Classification Problems - IEEE Rebooting Computing 2017
"What is Big Data Analytics and Why Should I Care?" - Big Data Analytics Tutorial Part 1
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
ICASSP 2012 Plenary-Dr. Stephane Mallat
Eliminate Operational Legacy Forever - Sasha Ratkovic - IEEE Sarnoff Symposium, 2019
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)
SIMD Programming in VOLK, the Vector-Optimized Library of Kernels
VisualDx Augmented Intelligence Project - Arthur Papier - IEEE EMBS at NIH, 2019
Adaptive Learning and Optimization for MI: From the Foundations to Complex Systems - Haibo He - WCCI 2016

IEEE-USA E-Books

  • Vector power in alternating-current circuits

    It has long been known that in any simple alternating-current circuit, the current and voltage may be conveniently regarded as rotatable vector quantities.1It is also known that the power in such circuits is not to be regarded as the vector product of the rotating vector voltage and rotating vector current.2It does not seem to have been pointed out, however, that, under certain restrictions, it is proper to regard the power in an alternating-current circuit as a non-rotating vector quantity. Moreover, it does not appear to be generally known, although the fact has not escaped notice, that the imaginary component of vector power, or so-called “wattless power” is, in a restricted sense, just as much power, and just as “wattful” as the real component.3

  • 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.

  • On the study of moving objects detection and pattern recognition using LS-SVM

    Based on pattern recognition theory and support vector machine(SVM) technology, moving objects automatic detection, recognition and classification method are discussed in detail. An algorithm of moving objects detection on the combination of double inter-frame difference dasiaorpsila operating and a new multi-sorts classification method-binary exponent classification are presented. By comparing SVM with BP neural network in vehicle classification, Experimental results showed that SVM algorithm improve recognition rate and the can recognize and classify moving objects rapidly and effectively.

  • Evaluating the supplier cooperative design ability using a novel support vector machine algorithm

    The supplier's cooperative design enables the enterprise to use supplier's technical ability fully, reduce the time of product coming into the market, improve quality, and reduce cost. Aiming at the problem of how to evaluate the supplier cooperative design ability (SCDA), this paper constructs the evaluating indices system that is composed of product concept and functional design support ability, product structure design and project support ability, and the process design and project support ability. To evaluate the SCDA scientifically and accurately, this paper proposes the multi-level classification evaluating model based on improved support vector machine (SVM), which uses the SVM classification combination in series and introduces the type weight factor and sample weight factor. The model not only solves the shortcomings of small sample, high dimension, nonlinear and local minima in the traditional model, but solves the wrong classification question caused by the number imbalance of training samples and data interference. The SCDA evaluating results of 12 enterprises in Hebei Province show that the model is simple and feasible, improve the evaluating accuracy and efficiency.

  • Gear Intelligent Fault Diagnosis Based on Support Vector Machines

    Support vector machines (SVM) was used in fault intelligent diagnosis of gear. The main research in feature extraction and data preprocess. The feature value of time domain includes peak to peak value, absolute average, square root amplitude, mean square amplitude. The feature value of frequency domain is MSF. The SVM method was used for detecting the gear case. The feature of time and the feature of frequent was be used. Through designed a band-pass filter, the feature of gear case's signal was extracted, including feature of time and feature of frequent. The results showed that the reference and fault stations of fan can be distinguished clearly in the SVM diagram. The results showed that it was better than that signals which didn't use filter.



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