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| February 23
| February 23
| Jay Pujara
| Jay Pujara
|
| Using Classifier Cascades for Scalable E-Mail Classification
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|-
| March 1
| March 1
In this tutorial we describe how several computer vision problems can be intuitively formulated as Markov Random Fields. Inference in such models can be transformed to an energy minimization problem. Under some conditions, graph cut methods can be used to find the minimum of the energy function and, in turn, the most probable assignment for its variables. In addition, we will briefly cover some of the recent advances in the application of graph cuts to a wider set of energy functions.
In this tutorial we describe how several computer vision problems can be intuitively formulated as Markov Random Fields. Inference in such models can be transformed to an energy minimization problem. Under some conditions, graph cut methods can be used to find the minimum of the energy function and, in turn, the most probable assignment for its variables. In addition, we will briefly cover some of the recent advances in the application of graph cuts to a wider set of energy functions.
===Using Classifier Cascades for Scalable E-Mail Classification===
Speaker: [http://www.cs.umd.edu/~jay/ Jay Pujara] -- Date: February 23, 2012
In many real-world scenarios, we must make judgments in the presence of computational constraints. One common computational constraint arises when the features used to make a judgment each have differing acquisition costs, but there is a fixed total budget for a set of judgments. Particularly when there are a large number of classifications that must be made in a real-time, an intelligent strategy for optimizing accuracy versus computational costs is essential. E-mail classification is an area where accurate and timely results require such a trade-off. We identify two scenarios where intelligent feature acquisition can improve classifier performance. In granular classification we seek to classify e-mails with increasingly specific labels structured in a hierarchy, where each level of the hierarchy requires a different trade-off between cost and accuracy. In load-sensitive classification, we classify a set of instances within an arbitrary total budget for acquiring features. Our method, Adaptive Classifier Cascades (ACC), designs a policy to combine a series of base classifiers with increasing computational costs given a desired trade-off between cost and accuracy. Using this method, we learn a relationship between feature costs and label hierarchies, for granular classification and cost budgets, for load-sensitive classification. We evaluate our method on real-world e-mail datasets with realistic estimates of feature acquisition cost, and we demonstrate superior results when compared to baseline classifiers that do not have a granular, cost-sensitive feature acquisition policy.
