On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Rademacher and gaussian complexities: risk bounds and structural results
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Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
The Cauchy-Schwarz Master Class: An Introduction to the Art of Mathematical Inequalities
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Learning the Kernel Matrix with Semidefinite Programming
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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
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Learning the Kernel Function via Regularization
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Estimating the Support of a High-Dimensional Distribution
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Working Set Selection Using Second Order Information for Training Support Vector Machines
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Training a Support Vector Machine in the Primal
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Large Scale Multiple Kernel Learning
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Value Regularization and Fenchel Duality
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Optimized cutting plane algorithm for support vector machines
Proceedings of the 25th international conference on Machine learning
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
The Group-Lasso for generalized linear models: uniqueness of solutions and efficient algorithms
Proceedings of the 25th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
An Automated Combination of Kernels for Predicting Protein Subcellular Localization
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Convex multi-task feature learning
Machine Learning
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Toward a gold standard for promoter prediction evaluation
Bioinformatics
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
Machine Learning
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
The SHOGUN Machine Learning Toolbox
The Journal of Machine Learning Research
A unifying view of multiple kernel learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Variable Sparsity Kernel Learning
The Journal of Machine Learning Research
Learning interpretable SVMs for biological sequence classification
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
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
Non-sparse multiple kernel fisher discriminant analysis
The Journal of Machine Learning Research
Structured sparsity and generalization
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Incorporation of radius-info can be simple with SimpleMKL
Neurocomputing
Probabilistic and discriminative group-wise feature selection methods for credit risk analysis
Expert Systems with Applications: An International Journal
Multi-Kernel based feature selection for regression
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Multi-channel shape-flow kernel descriptors for robust video event detection and retrieval
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Efficient training of graph-regularized multitask SVMs
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Separable approximate optimization of support vector machines for distributed sensing
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Online learning with multiple kernels: A review
Neural Computation
Enhanced representation and multi-task learning for image annotation
Computer Vision and Image Understanding
Unsupervised non-parametric kernel learning algorithm
Knowledge-Based Systems
lp-norm multikernel learning approach for stock market price forecasting
Computational Intelligence and Neuroscience
Segmental multi-way local pooling for video recognition
Proceedings of the 21st ACM international conference on Multimedia
JKernelMachines: a simple framework for kernel machine
The Journal of Machine Learning Research
On the convergence rate of lp-norm multiple kernel learning
The Journal of Machine Learning Research
Eigenvalues perturbation of integral operator for kernel selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Toward supervised anomaly detection
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Boosted kernel for image categorization
Multimedia Tools and Applications
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Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability. Unfortunately, this l1-norm MKL is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we extend MKL to arbitrary norms. We devise new insights on the connection between several existing MKL formulations and develop two efficient interleaved optimization strategies for arbitrary norms, that is lp-norms with p ≥ 1. This interleaved optimization is much faster than the commonly used wrapper approaches, as demonstrated on several data sets. A theoretical analysis and an experiment on controlled artificial data shed light on the appropriateness of sparse, non-sparse and l∞-norm MKL in various scenarios. Importantly, empirical applications of lp-norm MKL to three real-world problems from computational biology show that non-sparse MKL achieves accuracies that surpass the state-of-the-art. Data sets, source code to reproduce the experiments, implementations of the algorithms, and further information are available at http://doc.ml.tu-berlin.de/nonsparse_mkl/.