We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memory of the kernelbased Percept...
We present a data structure enabling efficient nearest neighbor (NN) retrieval for bregman divergences. The family of bregman divergences includes many popular dissimilarity measu...
Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, also with known lab...
Alex J. Smola, Novi Quadrianto, Quoc V. Le, Tib&ea...
In multiple instance learning (MIL), how the instances determine the bag-labels is an essential issue, both algorithmically and intrinsically. In this paper, we show that the mech...
In this paper, we consider a smoothing kernelbased classification rule and propose an algorithm for optimizing the performance of the rule by learning the bandwidth of the smoothi...
Bharath K. Sriperumbudur, Omer A. Lang, Gert R. G....
While discriminative training (e.g., CRF, structural SVM) holds much promise for machine translation, image segmentation, and clustering, the complex inference these applications ...
In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semisupervised alignment" in that it results ...
We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs ...
Katherine A. Heller, Sinead Williamson, Zoubin Gha...
In EM and related algorithms, E-step computations distribute easily, because data items are independent given parameters. For very large data sets, however, even storing all of th...