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Augmented Kernel Matrix vs Classifier Fusion for Object Recognition
2011
Procedings of the British Machine Vision Conference 2011
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Augmented Kernel Matrix (AKM) has recently been proposed to accommodate for the fact that a single training example may have different importance in different feature spaces, in contrast to Multiple Kernel ...
Learning (MKL) that assigns the same weight to all examples in one feature space. ...
This research was supported by UK EPSRC EP/F0034 20/1, EP/F0694 21/1 and the BBC R&D grants. ...
doi:10.5244/c.25.60
dblp:conf/bmvc/AwaisYMK11
fatcat:jlfcxb26oncf3jmbsqbbpbb4um
Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification
[article]
2013
arXiv
pre-print
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In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. ...
By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which leads to a more straightforward solution of the problem in the primal space. ...
The process of learning the kernel weights while simultaneously minimizing the structural risk is known as the multiple kernel learning (MKL). ...
arXiv:1310.0890v1
fatcat:z6l2gd3nc5gabmwicviqie4z4e
Multiple Kernel Learning in the Primal for Multimodal Alzheimer's Disease Classification
2014
IEEE journal of biomedical and health informatics
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In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. ...
By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which leads to a more straightforward solution of the problem in the primal. ...
The process of learning the kernel weights while simultaneously minimizing the structural risk is known as the multiple kernel learning (MKL). ...
doi:10.1109/jbhi.2013.2285378
pmid:24132030
fatcat:er2i773g2vfzpoeirs4vojozma
Online-batch strongly convex Multi Kernel Learning
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Here we present a Multiclass Multi Kernel Learning (MKL) algorithm that obtains state-of-the-art performance in a considerably lower training time. ...
Thanks to this new setting, we can directly solve the problem in the primal formulation. ...
Acknowledgments The kernel matrixes of Caltech-101 were kindly provided by Peter Gehler, who we also thank for his useful comments. This work was sponsored by the EU project DIRAC IST-027787. ...
doi:10.1109/cvpr.2010.5540137
dblp:conf/cvpr/OrabonaJC10
fatcat:fx23gqyq3ffgnkpduhkkt4rpkq
AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach
2010
2010 20th International Conference on Pattern Recognition
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In this paper, we propose a novel large-margin based approach for multiple kernel learning (MKL) using biconvex optimization, called Adaptive Multiple Kernel Learning (AdaMKL). ...
To learn the weights for support vectors and the kernel coefficients, AdaMKL minimizes the objective function alternatively by learning one component while fixing the other at a time, and in this way only ...
Adaptive Multiple Kernel Learning In this paper, we focus on the binary classification using AdaMKL. We follow the notations in Section 1. ...
doi:10.1109/icpr.2010.521
dblp:conf/icpr/ZhangLD10
fatcat:xxp6fpu6zveu7jgsmbunke5dnu
Group Based Localized Multiple Kernel Learning Algorithm with lp-Norm
2016
International Journal of Innovative Computing, Information and Control
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Because the sparse constraint may lose useful kernels, we use an lp-norm constraint on the kernels and obtain non-sparse results to avoid losing useful kernels. ...
In this paper, we proposed a groupbased non-sparse localized multiple kernel learning algorithm to tackle the issues above. ...
The authors also gratefully acknowledge the helpful comments and suggestion of the reviewers, which have improved the presentation. ...
doi:10.24507/ijicic.12.06.1835
fatcat:3fkad4wyqzddposs33tej2lmna
RV-SVM: An Efficient Method for Learning Ranking SVM
[chapter]
2009
Lecture Notes in Computer Science
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In this paper, we first develop a 1-norm ranking SVM that is faster in testing than the standard ranking SVM, and propose Ranking Vector SVM (RV-SVM) that revises the 1-norm ranking SVM for faster training ...
We experimentally compared the RV-SVM with the state-of-the-art rank learning method provided in SVM-light. ...
We then develop the Ranking Vector SVM (RV-SVM) which uses as less support vectors as the 1-norm SVM and trains much faster than the standard 2-norm SVM. ...
doi:10.1007/978-3-642-01307-2_39
fatcat:mhpiqaljuraejflgazodtuafz4
Linear Programming SVM-ARMA$_{\rm 2K}$ With Application in Engine System Identification
2011
IEEE Transactions on Automation Science and Engineering
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In particular, the possible generalization of LP-SVM-ARMA 2K via more complex composite kernel functions is also discussed to meet the diversity of industrial practice. ...
Inspired by the triumphs of support vector learning methodology in pattern recognition and regression analysis, an innovational nonlinear systems identification algorithm, LP-SVM-ARMA 2K was developed ...
KERNEL where In our simulation, the subsystems (26)-(30) are learned by LP-SVR and QP-SVR, respectively. ...
doi:10.1109/tase.2011.2140105
fatcat:ak7twh24mnffnein6cnaylqqwu
NESVM: a Fast Gradient Method for Support Vector Machines
[article]
2010
arXiv
pre-print
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In particular, NESVM smoothes the non-differentiable hinge loss and ℓ_1-norm in the primal SVM. Then the optimal gradient method without any line search is adopted to solve the optimization. ...
In addition, NESVM is available for both linear and nonlinear kernels. ...
Smooth the ℓ 1 -norm In LP-SVM, the regularizer is defined by the sum of all ℓ 1 -norm ℓ(w i ) = |w i |, i.e., R(w) = p i=1 ℓ(w i ). ...
arXiv:1008.4000v1
fatcat:puci77wkhfhv7caiuwzsvtdnfy
NESVM: A Fast Gradient Method for Support Vector Machines
2010
2010 IEEE International Conference on Data Mining
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In particular, NESVM smoothes the nondifferentiable hinge loss and 1-norm in the primal SVM. Then the optimal gradient method without any line search is adopted to solve the optimization. ...
In addition, NESVM is available for both linear and nonlinear kernels. ...
Smooth the 1 -norm In LP-SVM, the regularizer is defined by the sum of all 1 -norm (w i ) = |w i |, i.e., R(w) = p i=1 (w i ). ...
doi:10.1109/icdm.2010.135
dblp:conf/icdm/ZhouTW10
fatcat:czrtkottbnh6fjpr3ucjk2445e
From Kernel Machines to Ensemble Learning
[article]
2014
arXiv
pre-print
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Here we propose a principled framework for directly constructing ensemble learning methods from kernel methods. ...
In other words, it is possible to design ensemble methods directly from SVM without any middle procedure. ...
in the primal objective of f lp . 2) The constraint of nonnegative w lead to the dual inequality constraint. ...
arXiv:1401.0767v1
fatcat:extsje6t4rdjdco2isrjle27we
Building Sparse Multiple-Kernel SVM Classifiers
2009
IEEE Transactions on Neural Networks
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In this paper, we further extend this idea by integrating with techniques from multiple-kernel learning (MKL). ...
The kernel function in this sparse SVM formulation no longer needs to be fixed but can be automatically learned as a linear combination of kernels. ...
Hence, unlike the formulation considered in Section IV-A, here we can learn a sparse multiple-kernel classifier by simply alternating between LP and standard SVM training. ...
doi:10.1109/tnn.2009.2014229
pmid:19342346
fatcat:ptgk2gq3lrao5hmzc6dukzzkfi
Novel Fusion Methods for Pattern Recognition
[chapter]
2011
Lecture Notes in Computer Science
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Over the last few years, several approaches have been proposed for information fusion including different variants of classifier level fusion (ensemble methods), stacking and multiple kernel learning ( ...
In this paper we propose a multiclass extension of binary ν-LPBoost, which learns the contribution of each class in each feature channel. ...
This research was supported by UK EPSRC EP/F0034 20/1, EP/F0694 21/1 and the BBC R&D grants. ...
doi:10.1007/978-3-642-23780-5_19
fatcat:3r2yudj2p5hvpj6qv5xrvbeoha
A Novel Multiple Kernel Learning Framework for Heterogeneous Feature Fusion and Variable Selection
2012
IEEE transactions on multimedia
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We propose a novel multiple kernel learning (MKL) algorithm with a group lasso regularizer, called group lasso regularized MKL (GL-MKL), for heterogeneous feature fusion and variable selection. ...
Adding a mixed 1,2 norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for recognition purposes. ...
ACKNOWLEDGEMENTS This work is supported in part by the National Science Council of Taiwan via NSC 99-2221-E-001-020 and NSC 100-2221-E-001-018-MY2. ...
doi:10.1109/tmm.2012.2188783
fatcat:l2iqmss3z5ex3dpo7ua27vofqy
Quantum Sparse Support Vector Machines
[article]
2022
arXiv
pre-print
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However, we prove that there are realistic scenarios in which a sparse linear classifier is expected to have high accuracy, and can be trained in sublinear time in terms of both the number of training ...
We analyze the computational complexity of Quantum Sparse Support Vector Machine, a linear classifier that minimizes the hinge loss and the L_1 norm of the feature weights vector and relies on a quantum ...
While the bound on the dual solution norm is a direct consequence of sparsity of the model, the i ξ i term in the primal solution norm in principle grows in proportion to the number of training samples ...
arXiv:1902.01879v4
fatcat:phqhimzcdjfghgc2o4zz5a7kci
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