To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Abstract. Clustering is often considered the most important unsupervised learning problem and several clustering algorithms have been proposed over the years. Many of these algorit...
This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We prop...
— Achieving high performance for out-of-core applications typically involves explicit management of the movement of data between the disk and the physical memory. We are developi...
Sriram Krishnamoorthy, Juan Piernas, Vinod Tippara...
In this work, we develop and evaluate a wide range of feature spaces for deriving Levinstyle verb classifications (Levin, 1993). We perform the classification experiments using Ba...