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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, byâ€¦ (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) consideredâ€¦ (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 Dirichletâ€¦ (More)

Given a covariance matrix, we consider the problem of maximizing the variance explained by a particular linear combination of the input variables while constraining the number of nonzero coefficientsâ€¦ (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 matrixâ€¦ (More)

- Douglas Turnbull, Luke Barrington, David A. Torres, Gert R. G. Lanckriet
- IEEE Transactions on Audio, Speech, and Languageâ€¦
- 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 aâ€¦ (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 experimentalâ€¦ (More)

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

A Hilbert space embedding for probability measures has recently been proposed (Gretton et al., 2007; Smola et al., 2007), with applications including dimensionality reduction, homogeneity testing andâ€¦ (More)

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

We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that rankings of dataâ€¦ (More)

We consider the non-metric multidimensional scaling problem: given a set of dissimilarities âˆ†, find an embedding whose inter-point Euclidean distances have the same ordering as âˆ†. In this paper, weâ€¦ (More)