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Multiple kernel learning and feature space denoising

Fei Yan, Josef Kittler, Krystian Mikolajczyk
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kpfdk5j5crfsbkta757tx2h56i" style="color: black;">2010 International Conference on Machine Learning and Cybernetics</a> </i> &nbsp;
We review a multiple kernel learning (MKL) technique called p regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space denoising on MKL.  ...  of variance kept by feature space denoising.  ...  Most MKL techniques learn kernel weights by maximising some measure of class separation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icmlc.2010.5580970">doi:10.1109/icmlc.2010.5580970</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icmlc/YanKM10.html">dblp:conf/icmlc/YanKM10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bmthndb7ijfevpesjc6cuaj4bi">fatcat:bmthndb7ijfevpesjc6cuaj4bi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170925115218/https://core.ac.uk/download/pdf/16517071.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d2/76/d276b8e0518da2d5d12c2ca3879be143797ba6cf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icmlc.2010.5580970"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

A DC-programming algorithm for kernel selection

Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil
<span title="">2006</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/v54jrmjbpzgodi4buoyaj7vrzm" style="color: black;">Proceedings of the 23rd international conference on Machine learning - ICML &#39;06</a> </i> &nbsp;
For example, the basic kernels could be isotropic Gaussians with variance in a prescribed interval or even Gaussians parameterized by multiple continuous parameters.  ...  We address the problem of learning a kernel for a given supervised learning task.  ...  ., 2005c ) that, for a wide class of loss functions, the minimum of the functional (4) over K(G) is bounded below and away from zero and generalization error bounds for the function (2) learned with the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1143844.1143850">doi:10.1145/1143844.1143850</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icml/ArgyriouHMP06.html">dblp:conf/icml/ArgyriouHMP06</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uil7yatk5fgophuyzekhq7dvga">fatcat:uil7yatk5fgophuyzekhq7dvga</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20071107095802/http://eprints.pascal-network.org/archive/00002719/01/dc-prog.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b1/fc/b1fcab63606efa8df7c76c15d569afe8c188349e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1143844.1143850"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification [article]

T M Feroz Ali, Kalpesh K Patel, Rajbabu Velmurugan, Subhasis Chaudhuri
<span title="2019-10-09">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
maximize the inter-class variance as well as minimize the intra-class variance.  ...  We also show how the efficiency of KFDA in metric learning can be further enhanced for person re-identification by using two simple yet efficient multiple kernel learning methods.  ...  Equivalently, KFDA learns a discriminative subspace to maximize the between class variance and minimize the within class variance.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.03923v1">arXiv:1910.03923v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4vw4r3pjwnahjcqvi4p6rvjvda">fatcat:4vw4r3pjwnahjcqvi4p6rvjvda</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200822005034/https://arxiv.org/pdf/1910.03923v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4a/fd/4afde5699b724d27dd909e07210f74d701171bd3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.03923v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Polychotomous kernel Fisher discriminant via top–down induction of binary tree

Zhao Lu, Lily Rui Liang, Gangbing Song, Shufang Wang
<span title="">2010</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nkrwe4pmozafvnd72yxufztpku" style="color: black;">Computers and Mathematics with Applications</a> </i> &nbsp;
That is where the kernel Fisher discriminant algorithm sets in the scenario of supervised learning.  ...  In this article, a new trail is blazed in developing innovative and effective algorithm for polychotomous kernel Fisher discriminant with the capability in estimating the posterior probabilities, which  ...  group clustering algorithm for multiple classes.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.camwa.2010.04.048">doi:10.1016/j.camwa.2010.04.048</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kbgpoumdhrb4jm24djgykth4du">fatcat:kbgpoumdhrb4jm24djgykth4du</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170929204648/http://publisher-connector.core.ac.uk/resourcesync/data/elsevier/pdf/812/aHR0cDovL2FwaS5lbHNldmllci5jb20vY29udGVudC9hcnRpY2xlL3BpaS9zMDg5ODEyMjExMDAwMzMwNQ%3D%3D.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/73/09/73090de10617d473f68552e331f55b1dc911ce2c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.camwa.2010.04.048"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Gradient Optimization for multiple kernel's parameters in support vector machines classification

A. Villa, M. Fauvel, J. Chanussot, P. Gamba, J. A. Benediktsson
<span title="">2008</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6i67v2zuujhfvocqaapmnsoifm" style="color: black;">IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium</a> </i> &nbsp;
The selection of multiple parameters is addressed, and an approach based on the analysis of the variance values of individual bands was proposed. Several state of the art kernels were tested.  ...  The subject of this work is the model selection of kernels with multiple parameters for support vector machines (SVM), with the purpose of classifying hyperspectral remote sensing data.  ...  influence on the learning capacity.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/igarss.2008.4779698">doi:10.1109/igarss.2008.4779698</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/igarss/VillaFCGB08.html">dblp:conf/igarss/VillaFCGB08</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/awtkm3xvvvg73jlrlkdmulfidm">fatcat:awtkm3xvvvg73jlrlkdmulfidm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809092609/http://www.gipsa-lab.grenoble-inp.fr/~jocelyn.chanussot/publis/igarss_08_villa_kernel.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3b/d2/3bd27fdf957882f47985d00df87fb460cd4f8ca1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/igarss.2008.4779698"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Kernel Null Space Methods for Novelty Detection

Paul Bodesheim, Alexander Freytag, Erik Rodner, Michael Kemmler, Joachim Denzler
<span title="">2013</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2013 IEEE Conference on Computer Vision and Pattern Recognition</a> </i> &nbsp;
Beside the possibility of modeling a single class, we are able to treat multiple known classes jointly and to detect novelties for a set of classes with a single model.  ...  In contrast to modeling the support of each known class individually, our approach makes use of a projection in a joint subspace where training samples of all known classes have zero intra-class variance  ...  First of all, we empirically verify that modeling multiple classes with a single one-class classifier as proposed in [10] is not appropriate, since learning individual one-class classifiers for each  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2013.433">doi:10.1109/cvpr.2013.433</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/BodesheimFRKD13.html">dblp:conf/cvpr/BodesheimFRKD13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ntfashyxvbdx5kif2mzuh73oay">fatcat:ntfashyxvbdx5kif2mzuh73oay</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160128141211/http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Bodesheim_Kernel_Null_Space_2013_CVPR_paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/99/12/9912d56aae416c55b07f8ccf8a6ab85cb6f45791.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2013.433"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Gaussian Processes for Object Categorization

Ashish Kapoor, Kristen Grauman, Raquel Urtasun, Trevor Darrell
<span title="2009-07-16">2009</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hfdglwo5wbbmta6wop52fam7a4" style="color: black;">International Journal of Computer Vision</a> </i> &nbsp;
Our probabilistic formulation provides a principled way to learn hyperparameters, which we utilize to learn an optimal combination of multiple covariance functions.  ...  Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty.  ...  Acknowledgements We thank the following for providing kernel matrices: Alex Berg, Anna Bosch, Jitendra Malik and Andrew Zisserman.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11263-009-0268-3">doi:10.1007/s11263-009-0268-3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/agm3qlh6fzba3jgo5fcfsgk5jm">fatcat:agm3qlh6fzba3jgo5fcfsgk5jm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180722030619/https://cloudfront.escholarship.org/dist/prd/content/qt7sd2n00g/qt7sd2n00g.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ba/1f/ba1fb75050e30721bb0508e7d9e8940303af02eb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11263-009-0268-3"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Symmetric RBF Classifier for Nonlinear Detection in Multiple-Antenna-Aided Systems

Sheng Chen, A. Wolfgang, C.J. Harris, L. Hanzo
<span title="">2008</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/22mhkeaq5zdqlmtti5oidj26fi" style="color: black;">IEEE Transactions on Neural Networks</a> </i> &nbsp;
In this paper, we propose a powerful symmetric radial basis function (RBF) classifier for nonlinear detection in the so-called "overloaded" multiple-antenna-aided communication systems.  ...  The proposed solution is capable of providing a signal-tonoise ratio (SNR) gain in excess of 8 dB against the powerful linear minimum bit error rate (BER) benchmark, when supporting four users with the  ...  As discussed in Section I, the inherent symmetry of the Bayesian detector in (13) is hard to learn by a black-box RBF or kernel classifier using noisy data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnn.2007.911745">doi:10.1109/tnn.2007.911745</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18467204">pmid:18467204</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qs24omitqvhrxbi6do5grxgwne">fatcat:qs24omitqvhrxbi6do5grxgwne</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171113110848/https://core.ac.uk/download/pdf/1508083.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/86/5e/865e9111027b23f3d2c45209a348a5b421d062ec.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tnn.2007.911745"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Minimum Variance Extreme Learning Machine for human action recognition

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</a> </i> &nbsp;
The proposed Minimum Variance Extreme Learning Machine classifier is evaluated in human action recognition, where we compare its performance with that of other ELM-based classifiers, as well as the kernel  ...  Based on the observation that the learning process of such networks can be considered to be a non-linear mapping of the training data to a high-dimensional feature space, followed by a data projection  ...  The performance of the proposed Minimum Variance Extreme Learning Machine algorithm has been evaluated in human action recognition by employing the BoW-based video representation and the χ 2 kernel function  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2014.6854640">doi:10.1109/icassp.2014.6854640</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/IosifidisTP14.html">dblp:conf/icassp/IosifidisTP14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wx34ikawmzcixmekhy7vrvkwnq">fatcat:wx34ikawmzcixmekhy7vrvkwnq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170814124324/http://mirlab.org/conference_papers/International_Conference/ICASSP%202014/papers/p5464-iosifidis.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f7/69/f7696ea7ba7af02b50cbeb881b6930399192eb7f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2014.6854640"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Reconciling modern machine-learning practice and the classical bias–variance trade-off

Mikhail Belkin, Daniel Hsu, Siyuan Ma, Soumik Mandal
<span title="2019-07-24">2019</span> <i title="Proceedings of the National Academy of Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nvtuoas5pbdsllkntnhizy4f4q" style="color: black;">Proceedings of the National Academy of Sciences of the United States of America</a> </i> &nbsp;
Indeed, one of the central tenets of the field, the bias–variance trade-off, appears to be at odds with the observed behavior of methods used in modern machine-learning practice.  ...  This connection between the performance and the structure of machine-learning models delineates the limits of classical analyses and has implications for both the theory and the practice of machine learning  ...  Remarkably, for kernel machines all 3 methods lead to the same minimum norm solution.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1073/pnas.1903070116">doi:10.1073/pnas.1903070116</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31341078">pmid:31341078</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6689936/">pmcid:PMC6689936</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6fbzu5o7ynhqjnkryssce3sy4e">fatcat:6fbzu5o7ynhqjnkryssce3sy4e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191228134545/http://www.cs.columbia.edu/~djhsu/papers/biasvariance-pnas.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f8/6f/f86f1748d1b6d22870f4347fd5d65314ba800583.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1073/pnas.1903070116"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6689936" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Data mapping by probabilistic modular networks and information-theoretic criteria

Yue Wang, Shang-Hung Lin, Huai Li, Sun-Yuan Kung
<span title="">1998</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gkn2pu46ozb4tmkxczacnmtvkq" style="color: black;">IEEE Transactions on Signal Processing</a> </i> &nbsp;
learning scheme.  ...  The class distribution functions are then obtained by learning generalized Gaussian mixtures, where a soft classification of the data is performed by an efficient incremental algorithm.  ...  In general, for the multiple class problem, the optimal Bayes classifier (minimum average error) classifies input patterns based on their posterior probabilities: Input is classified to class if (22)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/78.735311">doi:10.1109/78.735311</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/32mcfazlingbvhbqrwhwf6ubu4">fatcat:32mcfazlingbvhbqrwhwf6ubu4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170815121051/http://www.princeton.edu/~kung/papers_pdf/NetworkCoding/information_criteria_kung.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3f/8e/3f8e481ea845aa20704d8c93f6a3a72025219f64.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/78.735311"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Symmetric Kernel Detector for Multiple-Antenna Aided Beamforming Systems

S. Chen, A. Wolfgang, C.J. Harris, L. Hanzo
<span title="">2007</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qwvlxlyjhjb5fncg2asafcm5hm" style="color: black;">Neural Networks (IJCNN), International Joint Conference on</a> </i> &nbsp;
The proposed solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the powerfull linear minimum bit error rate benchmarker, when supporting five users with the aid of  ...  The classifier construction process is robust to the choice of the kernel width and is computationally efficient.  ...  It is clear that the inherent symmetry of the Bayesian detector in (9) is hard to learn by a blackbox kernel classifier.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ijcnn.2007.4371349">doi:10.1109/ijcnn.2007.4371349</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcnn/ChenWHH07.html">dblp:conf/ijcnn/ChenWHH07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tr5znapqlfactlswxnnblml6ta">fatcat:tr5znapqlfactlswxnnblml6ta</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20081031232541/http://eprints.ecs.soton.ac.uk/14420/1/ijcnn07-1642.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/7c/837c0afe9ba96287e28b2064e393b8053c21c5cc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ijcnn.2007.4371349"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Principal components null space analysis for image and video classification

N. Vaswani, R. Chellappa
<span title="">2006</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
A query is classified into class " " if its distance from the class' mean in the class' ANS is a minimum.  ...  In this PCA space, it finds for each class " ," an -dimensional subspace along which the class' intraclass variance is the smallest.  ...  As an extreme case of this situation, the minimum variance direction of one class could be a maximum variance direction for another.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2006.873449">doi:10.1109/tip.2006.873449</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/16830904">pmid:16830904</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vpfhu6ufc5dojl4nndr6zrfdmm">fatcat:vpfhu6ufc5dojl4nndr6zrfdmm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20110401191557/http://home.engineering.iastate.edu/~namrata/j2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d5/f2/d5f27bb17a875bc5764e5a578b57d47e67d58bd0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2006.873449"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study

Daniel F Polan, Samuel L Brady, Robert A Kaufman
<span title="2016-08-17">2016</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zuggg3zvsfaatpw6invuoevl7a" style="color: black;">Physics in Medicine and Biology</a> </i> &nbsp;
The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance evaluated over a voxel radius of 2 n , (n from 0 to 4), along with noise reduction and edge preserving  ...  The automated algorithm produced segmentation of seven material classes with a median DSC of 0.86 ± 0.03 for pediatric patient protocols, and 0.85 ± 0.04 for adult patient protocols.  ...  Four texture feature filter inputs: mean, minimum (min), maximum (max), and variance, along with a noise reduction Gaussian filter, were calculated for the six material classes based on varying kernel  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/0031-9155/61/17/6553">doi:10.1088/0031-9155/61/17/6553</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/27530679">pmid:27530679</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5039942/">pmcid:PMC5039942</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tl2sgotghvesrpfxkrp4q6wwvi">fatcat:tl2sgotghvesrpfxkrp4q6wwvi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200208234157/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5039942&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dc/53/dc5370e4a537b73af3a3a60fe09658a1402be41f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/0031-9155/61/17/6553"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> iop.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039942" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Learning to Combine Kernels for Object Categorization

Deyuan Zhang, Bingquan Liu, Chengjie Sun, Xiaolong Wang
<span title="2011-04-28">2011</span> <i title="Canadian Center of Science and Education"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/e3eberciqvbc3am2h4ordzdxu4" style="color: black;">Computer and Information Science</a> </i> &nbsp;
In this paper, we propose a filter framework "Learning to Align the Kernel to its Ideal Form(LAKIF)" to automatically learn the optimal linear combination of multiple kernels.  ...  Due to the high intra-class and inter-class variety of image categories, no single descriptor could be optimal in all situations.  ...  LAKIF use a novel kernel normalzation technique and learns the parameter by align the kernels to their ideal forms.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5539/cis.v4n3p116">doi:10.5539/cis.v4n3p116</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cpg3lqxbmjf4zbazjkzsv62h2u">fatcat:cpg3lqxbmjf4zbazjkzsv62h2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170922024124/http://ccsenet.org/journal/index.php/cis/article/viewFile/9925/7381/" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7f/31/7f310839e62c2623f6267b533047b323f61d2b27.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5539/cis.v4n3p116"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>
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