Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
A statistical framework for genomic data fusion
Bioinformatics
Protein function prediction via graph kernels
Bioinformatics
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Feature space perspectives for learning the kernel
Machine Learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Better multiclass classification via a margin-optimized single binary problem
Pattern Recognition Letters
A Multiple Kernel Learning Approach to Joint Multi-class Object Detection
Proceedings of the 30th DAGM symposium on Pattern Recognition
An Automated Combination of Kernels for Predicting Protein Subcellular Localization
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Automatic feature selection for anomaly detection
Proceedings of the 1st ACM workshop on Workshop on AISec
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Feature Selection for Density Level-Sets
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
A procedure of adaptive kernel combination with kernel-target alignment for object classification
Proceedings of the ACM International Conference on Image and Video Retrieval
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
On multiple kernel learning with multiple labels
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Representation and feature selection using multiple kernel learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bilinear formulated multiple kernel learning for multi-class classification problem
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Learning Multi-modal Similarity
The Journal of Machine Learning Research
Randomised manifold forests for principal angle-based face recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Novel fusion methods for pattern recognition
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Combining multiple kernels by augmenting the kernel matrix
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Foundations and Trends® in Computer Graphics and Vision
Multi kernel learning with online-batch optimization
The Journal of Machine Learning Research
Non-sparse multiple kernel fisher discriminant analysis
The Journal of Machine Learning Research
Algorithms for learning kernels based on centered alignment
The Journal of Machine Learning Research
Image classification by multimodal subspace learning
Pattern Recognition Letters
SPF-GMKL: generalized multiple kernel learning with a million kernels
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
Biologically inspired task oriented gist model for scene classification
Computer Vision and Image Understanding
View-Invariant action recognition using latent kernelized structural SVM
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Online Multiple Kernel Classification
Machine Learning
Online multi-modal distance learning for scalable multimedia retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
Online learning with multiple kernels: A review
Neural Computation
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
lp-norm multikernel learning approach for stock market price forecasting
Computational Intelligence and Neuroscience
Multiple spectral kernel learning and a gaussian complexity computation
Neural Computation
Analytic center cutting plane method for multiple kernel learning
Annals of Mathematics and Artificial Intelligence
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In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparse coefficients, it also generalizes feature selection to kernel selection. We propose MKL for joint feature maps. This provides a convenient and principled way for MKL with multiclass problems. In addition, we can exploit the joint feature map to learn kernels on output spaces. We show the equivalence of several different primal formulations including different regularizers. We present several optimization methods, and compare a convex quadratically constrained quadratic program (QCQP) and two semi-infinite linear programs (SILPs) on toy data, showing that the SILPs are faster than the QCQP. We then demonstrate the utility of our method by applying the SILP to three real world datasets.