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- W2187160236 abstract "In this position paper we introduce Active Inference, a paradigm for intelligently requesting human labels for inference and learning in situations with a nite budget for applying human resources for labeling cases. Many machine learning systems are applied to a stream of instances that can repeat, such as queries entered in a search engine or web pages for potential ad impressions. When a particular instance x can be subject to classication more than once, we have an additional complication to the budgeted learning setting. In such applications, frequently the distributions will be non-uniform; for instance, in the above applications the distributionsp(x) over examples are highly skewed and thus a few x’s result in a large percentage of the actual cases for prediction. In such settings, it may be benecial to allocate a human labeling budget selectively perform direct inference, requesting human labels on a selected subset of the instances to be provided to an end system in an eort to reduce misclassication cost on the x’s with the highest expected utility. In estimating the utility of labeling a particular x, one must consider three factors: misclassication cost, the probability of encountering x, p(x), and the valuex and its associated label may bring for (active) learning. We will discuss the illustrative application of machine learning for safe advertising, where there is a limited budget for acquiring ground-truth labels for labeling web-pages." @default.
- W2187160236 created "2016-06-24" @default.
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- W2187160236 date "2010-01-01" @default.
- W2187160236 modified "2023-09-27" @default.
- W2187160236 title "Active Inference and Learning for Classifying Streams" @default.
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