84 research outputs found
Learning the kernel with hyperkernels
This paper addresses the problem of choosing a kernel suitable for estimation with a support
vector machine, hence further automating machine learning. This goal is achieved by defining
a reproducing kernel Hilbert space on the space of kernels itself. Such a formulation leads to a
statistical estimation problem similar to the problem of minimizing a regularized risk functional.
We state the equivalent representer theorem for the choice of kernels and present a semidefinite
programming formulation of the resulting optimization problem. Several recipes for constructing
hyperkernels are provided, as well as the details of common machine learning problems. Experimental
results for classification, regression and novelty detection on UCI data show the feasibility
of our approach
Hierarchic Bayesian models for kernel learning
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source. We present a Bayesian hierarchical model which enables kernel learning and present effective variational Bayes estimators for regression and classification. Illustrative experiments demonstrate the utility of the proposed method
Combining dissimilarities in a hyper reproducing kernel hilbert space for complex human cancer prediction
9 páginas, 3 tablas.-- This is an open access article distributed under the Creative Commons Attribution
License.DNA microarrays provide rich profiles that are used in cancer prediction considering the gene expression levels across a collection of related samples. Support Vector Machines (SVM) have been applied to the classification of cancer samples with encouraging results. However, they rely on Euclidean distances that fail to reflect accurately the proximities among sample profiles. Then, non-Euclidean dissimilarities provide additional information that should be considered to reduce the misclassification errors. In this paper, we incorporate in the -SVM algorithm a linear combination of non-Euclidean dissimilarities. The weights of the combination are learnt in a (Hyper Reproducing Kernel Hilbert Space) HRKHS using a Semidefinite Programming algorithm. This approach allows us to incorporate a smoothing term that penalizes the complexity of the family of distances and avoids overfitting. The experimental results suggest that the method proposed helps to reduce the misclassification errors in several human cancer problems. © 2009 Manuel Mart́n-Merino et al.Financial support from Grant S02EIA-07L01.Peer Reviewe
Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction
DNA microarrays provide rich profiles that are used in
cancer prediction considering the gene expression levels
across a collection of related samples. Support Vector Machines
(SVM) have been applied to the classification of cancer
samples with encouraging results. However, they rely on
Euclidean distances that fail to reflect accurately the proximities
among sample profiles. Then, non-Euclidean dissimilarities
provide additional information that should be considered
to reduce the misclassification errors.
In this paper, we incorporate in the ν-SVM algorithm a
linear combination of non-Euclidean dissimilarities. The
weights of the combination are learnt in a (Hyper
Reproducing Kernel Hilbert Space) HRKHS using a Semidefinite
Programming algorithm. This approach allows us to incorporate
a smoothing term that penalizes the complexity of the
family of distances and avoids overfitting. The experimental results suggest that the method proposed
helps to reduce the misclassification errors in several
human cancer problems
Model selection of polynomial kernel regression
Polynomial kernel regression is one of the standard and state-of-the-art
learning strategies. However, as is well known, the choices of the degree of
polynomial kernel and the regularization parameter are still open in the realm
of model selection. The first aim of this paper is to develop a strategy to
select these parameters. On one hand, based on the worst-case learning rate
analysis, we show that the regularization term in polynomial kernel regression
is not necessary. In other words, the regularization parameter can decrease
arbitrarily fast when the degree of the polynomial kernel is suitable tuned. On
the other hand,taking account of the implementation of the algorithm, the
regularization term is required. Summarily, the effect of the regularization
term in polynomial kernel regression is only to circumvent the " ill-condition"
of the kernel matrix. Based on this, the second purpose of this paper is to
propose a new model selection strategy, and then design an efficient learning
algorithm. Both theoretical and experimental analysis show that the new
strategy outperforms the previous one. Theoretically, we prove that the new
learning strategy is almost optimal if the regression function is smooth.
Experimentally, it is shown that the new strategy can significantly reduce the
computational burden without loss of generalization capability.Comment: 29 pages, 4 figure
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