When concepts combine

Learning When Concepts Abound

Many learning tasks, such as large-scale text categorization and word prediction, can benefit from efficient training and classification when the number of classes, in addition to instances and features, is large, that is, in the thousands and beyond. We investigate the learning of sparse class indices to address this challenge. An index is a mapping from features to classes. We compare the ind...

When Does It Make Sense to Linearly Combine Relevance Scores?

Real-Time Learning when Concepts Shift

We are interested in real-time learning problems where the underlying stochastic process, which generates the target concept, changes over time. We want our learner to detect when a change has occurred, thus realizing that the learned concept no longer fits the observed data. Our initial approach to this problem has been to analyze offline methods for addressing concept shifts and to apply them...

Real-time Learning when Concepts Shift

We are interested in real-time learning problems where the underlying stochastic process, which generates the target concept, changes over time. We want our learner to detect when a change has occurred, thus realizing that the learned concept no longer fits the observed data. Our initial approach to this problem has been to analyze offline approaches to addressing concept shifts and to apply th...

To Combine Forecasts or to Combine Information?

When the objective is to forecast a variable of interest but with many explanatory variables available, one could possibly improve the forecast by carefully integrating them. There are generally two directions one could proceed: combination of forecasts (CF) or combination of information (CI). CF combines forecasts generated from simple models each incorporating a part of the whole information ...