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. 1.

The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several methods have been proposed for combining kernels from heterogeneous data sources. However, all of these methods produce stationary combinations; i.e., the relative weights of the various kernels do not vary among input examples. This article proposes a method for combining multiple kernels in a nonstationary fashion. The approach uses a large-margin latentvariable generative model within the maximum entropy discrimination (MED) framework. Latent parameter estimation is rendered tractable by variational bounds and an iterative optimization procedure. The classifier we use is a log-ratio of Gaussian mixtures, in which each component is implicitly mapped via a Mercer kernel function. We show that the support vector machine is a special case of this model. In this approach, discriminative parameter estimation is feasible via a fast sequential minimal optimization algorithm. Empirical results are presented on synthetic data, several benchmarks, and on a protein function annotation task. 1.

Support Vector Machine-based speaker verification (SV) has become a standard approach in recent years. These systems typically use dynamic kernels to handle the dynamic nature of the speech utterances. This paper shows that many of these kernels fall into one of two general classes, derivative and parametric kernels. The attributes of these classes are contrasted and the conditions under which the two forms of kernel are identical are described. By avoiding these conditions gains may be obtained by combining derivative and parametric kernels. One combination strategy is to combine at the kernel level. This paper describes a maximum-margin based scheme for learning kernel weights for the SV task. Various dynamic kernels and combinations were evaluated on the NIST 2002 SRE task, including derivative and parametric kernels based upon different model structures. The best overall performance was 7.78 % EER achieved when combining five kernels.

Abstract not found

Many speaker verification (SV) systems combine multiple classifiers using score-fusion to improve system performance. For SVM classifiers, an alternative strategy is to combine at the kernel level. This involves finding a suitable kernel weighting, known as Multiple Kernel Learning (MKL). Recently, an efficient maximum-margin scheme for MKL has been proposed. This work examines several refinements to this scheme for SV. The standard scheme has a known tendency towards sparse weightings, which may not be optimal for SV. A regularisation term is proposed, allowing the appropriate level of sparsity to be selected. Cross-speaker tying of kernel weights is also applied to improve robustness. Various combinations of dynamic kernels were evaluated, including derivative and parametric kernels based upon different model structures. The performance achieved on the NIST 2002 SRE when combining five kernels was 7.78 % EER.

Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer. Here we utilize the multiclass support vector machine (m-SVM) method to directly solve protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. We further propose a general class of protein sequence kernels which considers all motifs, including motifs with gaps. Instead of heuristically selecting one or a few kernels from this family, we utilize a recent extension of SVMs that optimizes over multiple kernels simultaneously. This way, we automatically search over families of possible amino acid motifs. We compare our automated approach to three other predictors on four different datasets, and show that we perform better than the current state of the art. Further, our method provides some insights as to which sequence motifs are most useful for determining subcellular localization, which are in agreement with biological reasoning. Data files, kernel matrices and open source software are available at

Abstract. Concept indexing in multimedia libraries is very useful for users searching and browsing but it is a very challenging research problem as well. Combining several modalities, features or concepts is one of the key issues for bridging the gap between signal and semantics. In this paper, we present three fusion schemes inspired from the classical early and late fusion schemes. First, we present a kernel-based fusion scheme which takes advantage of the kernel basis of classifiers such as SVMs. Second, we integrate a new normalization process into the early fusion scheme. Third, we present a contextual late fusion scheme to merge classification scores of several concepts. We conducted experiments in the framework of the official TRECVID’06 evaluation campaign and we obtained significant improvements with the proposed fusion schemes relatively to usual fusion schemes. 1

Abstract. Protein subcellular localization is a crucial ingredient to many important inferences about cellular processes, including prediction of protein function and protein interactions. While many predictive computational tools have been proposed, they tend to have complicated architectures and require many design decisions from the developer. We propose an elegant and fully automated approach to building a prediction system for protein subcellular localization. We propose a new class of protein sequence kernels which considers all motifs including motifs with gaps. This class of kernels allows the inclusion of pairwise amino acid distances into their computation. We further propose a multiclass support vector machine method which directly solves protein subcellular localization without resorting to the common approach of splitting the problem into several binary classification problems. To automatically search over families of possible amino acid motifs, we generalize our method to optimize over multiple kernels at the same time. We compare our automated approach to four other predictors on three different datasets. 1

Abstract — This paper presents a pedestrian detection method based on the multiple kernel framework. This approach enables us to select and combine different kinds of image representations. The combination is done through a linear combination of kernels, weighted according to the relevance of kernels. After having presented some descriptors and detailed the multiple kernel framework, we propose three different applications concerning combination of representations, automatic parameters setting and feature selection. We then show that the MKL framework enable us to apply a model selection and improve the performance. I.

Multiple Kernel Learning (MKL) aims to learn kernel machines for solving a real machine learning problem (e.g. classification) by exploring the combinations of multiple kernels. The traditional MKL approach is in general “shallow ” in the sense that the target kernel is simply a linear (or convex) combination of some base kernels. In this paper, we investigate a framework of Multi-Layer Multiple Kernel Learning (MLMKL) that aims to learn “deep ” kernel machines by exploring the combinations of multiple kernels in a multi-layer structure, which goes beyond the conventional MKL approach. Through a multiple layer mapping, the proposed MLMKL framework offers higher flexibility than the regular MKL for finding the optimal kernel for applications. As the first attempt to this new MKL framework, we present a Two-Layer Multiple Kernel Learning (2LMKL) method together with two efficient algorithms for classification tasks. We analyze their generalization performances and have conducted an extensive set of experiments over 16 benchmark datasets, in which encouraging results showed that our method performed better than the conventional MKL methods. 1