#### Filter Results:

- Full text PDF available (121)

#### Publication Year

2001

2016

- This year (0)
- Last 5 years (39)
- Last 10 years (105)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Cell Type

#### Data Set Used

#### Key Phrases

Learn More

- 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)

- 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 covari-ance 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)

- 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, 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)

- 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)

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

We formulate metric learning as a learning to rank problem. This leads to an algorithm which can be used for query-by-example information retrieval problems. The algorithm supports general ranking loss measures in addition to binary kNN.

We consider the non-metric multidimensional scaling problem: given a set of dissimilarities ∆, find an embedding whose inter-point Eu-clidean distances have the same ordering as ∆. In this paper, we look at a generalization of this problem in which only a set of order relations of the form d ij < d kl are provided. Unlike the original problem, these order… (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)