The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Text classification using string kernels
The Journal of Machine Learning Research
Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data
Neural Processing Letters
A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension
IEEE Transactions on Knowledge and Data Engineering
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Column-generation boosting methods for mixture of kernels
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
A statistical framework for genomic data fusion
Bioinformatics
Kernel methods for predicting protein--protein interactions
Bioinformatics
Hierarchic Bayesian models for kernel learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
A DC-programming algorithm for kernel selection
ICML '06 Proceedings of the 23rd international conference on Machine learning
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Nonstationary kernel combination
ICML '06 Proceedings of the 23rd international conference on Machine learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A Direct Method for Building Sparse Kernel Learning Algorithms
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Discriminant kernel and regularization parameter learning via semidefinite programming
Proceedings of the 24th international conference on Machine learning
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Glycan classification with tree kernels
Bioinformatics
Kernel-based data fusion for gene prioritization
Bioinformatics
Probabilistic multi-class multi-kernel learning
Bioinformatics
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
An efficient kernel matrix evaluation measure
Pattern Recognition
An Automated Combination of Kernels for Predicting Protein Subcellular Localization
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
A New Multiple Kernel Approach for Visual Concept Learning
MMM '09 Proceedings of the 15th International Multimedia Modeling Conference on Advances in Multimedia Modeling
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Partial order embedding with multiple kernels
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Non-monotonic feature selection
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Combining feature spaces for classification
Pattern Recognition
A Comparison of L_1 Norm and L_2 Norm Multiple Kernel SVMs in Image and Video Classification
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
Kernel based support vector machine via semidefinite programming: Application to medical diagnosis
Computers and Operations Research
Machine Learning
Sparse Multiple Kernel Learning for Signal Processing Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel combination versus classifier combination
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Face verification with a kernel fusion method
Pattern Recognition Letters
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Per-sample multiple kernel approach for visual concept learning
Journal on Image and Video Processing - Special issue on selected papers from multimedia modeling conference 2009
Learning convex combinations of continuously parameterized basic kernels
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Combining Derivative and Parametric Kernels for Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
Efficient hyperkernel learning using second-order cone programming
IEEE Transactions on Neural Networks
Combining data sources nonlinearly for cell nucleus classification of renal cell carcinoma
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Multitask learning using regularized multiple kernel learning
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Probabilistic and discriminative group-wise feature selection methods for credit risk analysis
Expert Systems with Applications: An International Journal
Transductive multi-label ensemble classification for protein function prediction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-Kernel based feature selection for regression
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Greedy unsupervised multiple kernel learning
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
A pattern mining based integrative framework for biomarker discovery
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Visual knowledge transfer among multiple cameras for people counting with occlusion handling
Proceedings of the 20th ACM international conference on Multimedia
Compact kernel hashing with multiple features
Proceedings of the 20th ACM international conference on Multimedia
Localized algorithms for multiple kernel learning
Pattern Recognition
Simultaneous learning of localized multiple kernels and classifier with weighted regularization
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Online learning with multiple kernels: A review
Neural Computation
New empirical nonparametric kernels for support vector machine classification
Applied Soft Computing
Rank prediction for semantically annotated resources
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Neurocomputing
Training Lp norm multiple kernel learning in the primal
Neural Networks
Multiple feature kernel hashing for large-scale visual search
Pattern Recognition
Supervised word sense disambiguation using semantic diffusion kernel
Engineering Applications of Artificial Intelligence
Using LR-based discriminant kernel methods with applications to speaker verification
Speech Communication
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A nested heuristic for parameter tuning in Support Vector Machines
Computers and Operations Research
Human activity recognition using multi-features and multiple kernel learning
Pattern Recognition
Learning kernels on extended Reeb graphs for 3d shape classification and retrieval
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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In recent years, several methods have been proposed to combine multiple kernels instead of using a single one. These different kernels may correspond to using different notions of similarity or may be using information coming from multiple sources (different representations or different feature subsets). In trying to organize and highlight the similarities and differences between them, we give a taxonomy of and review several multiple kernel learning algorithms. We perform experiments on real data sets for better illustration and comparison of existing algorithms. We see that though there may not be large differences in terms of accuracy, there is difference between them in complexity as given by the number of stored support vectors, the sparsity of the solution as given by the number of used kernels, and training time complexity. We see that overall, using multiple kernels instead of a single one is useful and believe that combining kernels in a nonlinear or data-dependent way seems more promising than linear combination in fusing information provided by simple linear kernels, whereas linear methods are more reasonable when combining complex Gaussian kernels.