This paper investigates the application of randomized algorithms for large scale SVM learning. The key contribution of the paper is to show that, by using ideas random projections...
— Designing a localization system for a low-cost robotic consumer product poses a major challenge. In previous work, we introduced Vector Field SLAM [5], a system for simultaneou...
Jens-Steffen Gutmann, Gabriel Brisson, Ethan Eade,...
We have developed a new Linear Support Vector Machine (SVM) training algorithm called OCAS. Its computational effort scales linearly with the sample size. In an extensive empirica...
Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification a...
We propose a distributed parallel support vector machine (DPSVM) training mechanism in a configurable network environment for distributed data mining. The basic idea is to exchange...