1 Oct 06:53
[Dbworld] Call for papers: NIPS 2008 Workshop on Cost-Sensitive Learning
==Call for Papers: NIPS 2008 Workshop on Cost-Sensitive Learning== http://www.cs.iastate.edu/~oksayakh/cslworkshop_nips2008.html Description and background ------------------------ The goal of cost-sensitive learning is to minimize data acquisition costs while maximizing the accuracy of the learner/predictor. Many fields in machine learning attempt to solve cost-sensitive learning with strong simplifying assumptions. For example, in semi-supervised learning, class-labels are assumed to be expensive and features are implicitly assumed to have zero cost. In active learning, labels are again assumed to be expensive; however the learner may ask an oracle to reveal a label for unlabeled data for selected examples. Active feature acquisition assumes that obtaining features is expensive (but typically all features are assumed to be equally expensive), and the learner identifies instances for which complete information is most informative to classify a particular test sample. Inductive transfer learning and domain adaptation methods assume that training data for a particular task is expensive or but other data from other domains may be cheaper (although relative costs are usually not explicitly modeled). Cascaded classifier architectures are primarily designed in order to reduce the cost! of acquiring features to classify a sample (a sample may be classified the moment the available data is sufficient to provide sufficient classification confidence, without waiting for all features to be obtained). There is an important but neglected common thread linking all of these different research communities. In particular, all these learning methods are motivated by the need to minimize the cost of data acquisition in many different application domains such as computer-aided medical diagnosis, computational linguistics, computational biology, and computer vision. Although all of these areas have felt the need for a principled solution to the problem, the partial solutions that have tried to solve the problem (eg semi-supervised learning, active learning, multi-task inductive transfer etc) rarely model the cost explicitly, and very little effort has been expended on modeling application specific characteristics. Recently to some papers have started modeling the acquisition costs directly, but there is a lot of scope(Continue reading)
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