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- Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan
- Journal of Machine Learning Research
- 2002

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and… (More)

While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to… (More)

- Nikhil Rasiwasia, Jose Costa Pereira, +4 authors Nuno Vasconcelos
- ACM Multimedia
- 2010

The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly modeling… (More)

- Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan
- Journal of Machine Learning Research
- 2002

When constructing a classifier, the probability of correct classification of future data points should be maximized. We consider a binary classification problem where the mean and covariance matrix of each class are assumed to be known. No further assumptions are made with respect to the class-conditional distributions. Misclassification probabilities are… (More)

- Gert R. G. Lanckriet, Tijl De Bie, Nello Cristianini, Michael I. Jordan, William Stafford Noble
- Bioinformatics
- 2004

MOTIVATION
During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data.
RESULTS
This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each… (More)

- Douglas Turnbull, Luke Barrington, David A. Torres, Gert R. G. Lanckriet
- IEEE Transactions on Audio, Speech, and Language…
- 2008

We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given a text-based query. We consider the related tasks of content-based audio annotation and retrieval as one supervised multiclass, multilabel problem in which we model… (More)

- Brian McFee, Gert R. G. Lanckriet
- ICML
- 2010

[1] Tsochantaridis, Ioannis, Joachims, Thorsten, Hofmann, Thomas, and Altun, Yasemin. Large margin methods for structured and interdependent output variables. JMLR, 6: 1453-1484, 2005. [2] Joachims, Thorsten, Finley, Thomas, and Yu, Chun-nam John. Cuttingplane training of structural SVMs. Machine Learning, 77(1):27-59, 2009. References Da ta Matchings… (More)

- Gert R. G. Lanckriet, Minghua Deng, Nello Cristianini, Michael I. Jordan, William Stafford Noble
- Pacific Symposium on Biocomputing
- 2004

Kernel methods provide a principled framework in which to represent many types of data, including vectors, strings, trees and graphs. As such, these methods are useful for drawing inferences about biological phenomena. We describe a method for combining multiple kernel representations in an optimal fashion, by formulating the problem as a convex… (More)

- Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R. G. Lanckriet
- Journal of Machine Learning Research
- 2010

A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing, and independence testing. This embedding represents any probability measure as a mean element in a reproducing kernel Hilbert space (RKHS). A pseudometric on the space of probability measures can be… (More)

- Jose Costa Pereira, Emanuele Coviello, +4 authors Nuno Vasconcelos
- IEEE Transactions on Pattern Analysis and Machine…
- 2014

The problem of cross-modal retrieval from multimedia repositories is considered. This problem addresses the design of retrieval systems that support queries across content modalities, for example, using an image to search for texts. A mathematical formulation is proposed, equating the design of cross-modal retrieval systems to that of isomorphic feature… (More)