We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithm...
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Clustering performance can often be greatly improved by
leveraging side information. In this paper, we consider constrained
clustering with pairwise constraints, which specify
s...
In this paper, we describe a creativity workshop that was used in a large research project, called APOSDLE, to generate creative ideas and requirements for a workintegrated learni...
Sara Jones, Perry Lynch, Neil A. M. Maiden, Stefan...
Deduplication, a key operation in integrating data from multiple sources, is a time-consuming, labor-intensive and domainspecific operation. We present our design of alias that us...