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Multiple kernel learning, conic duality, and the SMO algorithm
2004
Twenty-first international conference on Machine learning - ICML '04
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Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to a convex ...
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. ...
Acknowledgements We wish to acknowledge support from a grant from Intel Corporation, and a graduate fellowship to Francis Bach from Microsoft Research. ...
doi:10.1145/1015330.1015424
dblp:conf/icml/BachLJ04
fatcat:vdn7dckdxfd4lhvm3nzws4zjpe
A General and Efficient Multiple Kernel Learning Algorithm
2005
Neural Information Processing Systems
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While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. ...
Experimental results show that the proposed algorithm helps for automatic model selection, improving the interpretability of the learning result and works for hundred thousands of examples or hundreds ...
Acknowledgments The authors gratefully acknowledge partial support from the PASCAL Network of Excellence (EU #506778), DFG grants JA 379 / 13-2 and MU 987/2-1. ...
dblp:conf/nips/SonnenburgRS05
fatcat:xiraije4knhezcohkmxzpvqcra
Multiple Kernel Learning Algorithms
2011
Journal of machine learning research
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In trying to organize and highlight the similarities and differences between them, we give a taxonomy of and review several multiple kernel learning algorithms. ...
as given by the number of used kernels, and training time complexity. ...
and the Scientific and Technological Research Council of Turkey (T ÜB İTAK) under Grant EEEAG ...
dblp:journals/jmlr/GonenA11
fatcat:rx5y5cxvhfbylhmppqqfixam4m
SpicyMKL
[article]
2011
arXiv
pre-print
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We propose a new optimization algorithm for Multiple Kernel Learning (MKL) called SpicyMKL, which is applicable to general convex loss functions and general types of regularization. ...
SpicyMKL can be viewed as a proximal minimization method and converges super-linearly. The cost of inner minimization is roughly proportional to the number of active kernels. ...
[1] casted the problem as a second order conic programming (SOCP) problem and proposed an SMO-like algorithm to deal with medium-scale problems. ...
arXiv:0909.5026v2
fatcat:gkkzla3fxrgetgoromtjfypa2y
Support Vector Machine Classification with Indefinite Kernels
[article]
2009
arXiv
pre-print
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Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. ...
This can be interpreted as a penalized kernel learning problem where indefinite kernel matrices are treated as a noisy observations of a true Mercer kernel. ...
Acknowledgements We are very grateful to Mátyás Sustik for his rank-one update eigenvalue decomposition code and to Jianhui Chen and Jieping Ye for their SIQCLP Matlab code. ...
arXiv:0804.0188v2
fatcat:qrovfurtsjcz5ewy2pvvm2ptgq
Support vector machine classification with indefinite kernels
2009
Mathematical Programming Computation
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Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix used in forming the loss. ...
This can be interpreted as a penalized kernel learning problem where indefinite kernel matrices are treated as a noisy observations of a true Mercer kernel. ...
The authors would also like to acknowledge support from NSF grant DMS-0625352, ONR grant number N00014-07-1-0150, a Peek junior faculty fellowship and a gift from Google, Inc. ...
doi:10.1007/s12532-009-0005-5
fatcat:k4pgrqqlrzbjtjuwxjuewybo7a
Nonlinear Pairwise Layer and Its Training for Kernel Learning
2018
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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learning and standard SVM optimization. ...
Experimentally, we find that the proposed structure outperforms other state-ofthe-art kernel-based algorithms on various benchmark datasets, and thus the effectiveness of the incorporated pairwise layer ...
Acknowledgements This work was supported in part by the National Natural Science Foundation of China (No. 61572315, 6151101179, 61603248, 61602246, 61703077), in part by the Natural Science Foundation ...
doi:10.1609/aaai.v32i1.11622
fatcat:kcnx6vdhevdbzasfxxsizwgdrq
Margin and Radius Based Multiple Kernel Learning
[chapter]
2009
Lecture Notes in Computer Science
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One of the approaches proposed to address this problem is Multiple Kernel Learning (MKL) in which several kernels are combined adaptively for a given dataset. ...
We present a novel MKL algorithm that optimizes the error bound taking account of both the margin and the radius. ...
The work reported in this paper was partially funded by the European Commission through EU projects DropTop (FP6-037739), De-bugIT (FP7-217139) and e-LICO (FP7-231519). ...
doi:10.1007/978-3-642-04180-8_39
fatcat:g4ocifc57vc5rbitgv2cxim4x4
Learning kernels from indefinite similarities
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
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These indefinite kernels can be problematic for standard kernel-based algorithms as the optimization problems become nonconvex and the underlying theory is invalidated. ...
In order to adapt kernel methods for similarity-based learning, we introduce a method that aims to simultaneously find a reproducing kernel Hilbert space based on the given similarities and train a classifier ...
Acknowledgments This work was funded by the Office of Naval Research. ...
doi:10.1145/1553374.1553393
dblp:conf/icml/ChenGR09
fatcat:lzwprwl4fraixk7dvcprwckrie
A Convex Feature Learning Formulation for Latent Task Structure Discovery
[article]
2012
arXiv
pre-print
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Empirical results on benchmark datasets show that the proposed formulation achieves good generalization and outperforms state-of-the-art multi-task learning algorithms in some cases. ...
This paper considers the multi-task learning problem and in the setting where some relevant features could be shared across few related tasks. ...
Following the set-up of Multiple Kernel Learning (MKL) (Bach et al., 2004) , the feature space in each group is taken to be that induced by a conic combination of a given set of base kernels. ...
arXiv:1206.4611v1
fatcat:vczxybtakbgzbpwgwrvssblwmy
SpicyMKL: a fast algorithm for Multiple Kernel Learning with thousands of kernels
2011
Machine Learning
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We propose a new optimization algorithm for Multiple Kernel Learning (MKL) called SpicyMKL, which is applicable to general convex loss functions and general types of regularization. ...
SpicyMKL can be viewed as a proximal minimization method and converges super-linearly. The cost of inner minimization is roughly proportional to the number of active kernels. ...
Acknowledgements We would like to thank anonymous reviewers for their constructive comments, which improved the quality of this paper. ...
doi:10.1007/s10994-011-5252-9
fatcat:en6tlug7ebexhmqbscdktxfq6m
Generalized hierarchical kernel learning
2015
Journal of machine learning research
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This paper generalizes the framework of Hierarchical Kernel Learning (HKL) and illustrates its utility in the domain of rule learning. ...
HKL involves Multiple Kernel Learning over a set of given base kernels assumed to be embedded on a directed acyclic graph. ...
Acknowledgments We thank the anonymous reviewers for the valuable comments. We acknowledge Chiranjib Bhattacharyya for initiating discussions on optimal learning of rule ensembles. ...
dblp:journals/jmlr/JawanpuriaNR15
fatcat:yymoxaqhezg2zi2pta6fgabi6e
Genomic Prediction of Quantitative Traits using Sparse and Locally Epistatic Models
[article]
2014
arXiv
pre-print
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To this end, we have used semi-parametric mixed models with multiple local genomic relationship matrices with hierarchical designs and lasso post-processing for sparsity in the final model. ...
The models introduced here aim to estimate local epistatic line heritability by using the genetic map information and combine the local additive and epistatic effects. ...
Multiple kernel learning, conic duality, and the smo algorithm. In Proceedings of the twenty-first international conference on Machine learning, page 6. ACM, 2004. [2] G. Blanchard and D. Geman. ...
arXiv:1402.2026v1
fatcat:bzv6zks6xfhqnp2wded4cb66fe
Fast SVM Trained by Divide-and-Conquer Anchors
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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Experimental results on multiple datasets demonstrate that our DCA-SVM is faster than the state-of-the-art algorithms without notably decreasing the accuracy of classification results. ...
In this paper, we propose to choose the representative points which are noted as anchors obtained from non-negative matrix factorization (NMF) in a divide-and-conquer framework, and then use the anchors ...
Acknowledgements This research is partially supported by grants from NS-FC 61375026 and 2015BAF15B00, and Australian Research Council Projects FT-130101457, DP-140102164, LP-150100671. ...
doi:10.24963/ijcai.2017/323
dblp:conf/ijcai/LiuX0T17
fatcat:jfyqcqq3lrc3bgyd6onwhf6gza
Learning with Support Vector Machines
2011
Synthesis Lectures on Artificial Intelligence and Machine Learning
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A special thanks to Simon Rogers of the University of Glasgow and Mark Girolami, Massimiliano Pontil and other members of the Centre for Computational Statistics and Machine Learning at University College ...
years and to our many collaborators, particularly Nello Cristianini, Tijl de Bie and other members of the Intelligent Systems Laboratory, University of Bristol. ...
The latter is referred to as multiple kernel learning (MKL). ...
doi:10.2200/s00324ed1v01y201102aim010
fatcat:urqxw5ew3vclvkeluvrh72fxei
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