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F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then(More)
POMDPs provide a principled framework for planning under uncertainty, but are computationally intractable, due to the “curse of dimensionality” and the “curse of history”. This paper presents an online search algorithm that alleviates these difficulties by focusing on a set of sampled scenarios. The execution of all policies on the sampled scenarios is(More)
Dependencies among neighbouring labels in a sequence is an important source of information for sequence labeling problems. However, only dependencies between adjacent labels are commonly exploited in practice because of the high computational complexity of typical inference algorithms when longer distance dependencies are taken into account. In this paper,(More)
Following the launch of the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission on 2 November 2009, SMOS soil moisture products need to be rigorously validated at the satellite’s approximately 45 km scale and disaggregation techniques for producing maps with finer resolutions tested. The Australian Airborne Cal/val Experiments for SMOS(More)
F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then(More)
Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retraining the classifier. It is often used in situations where labeled training data is scarce but unlabeled data is abundant. In this paper, we consider the problem of domain(More)
We consider adaptive pool-based active learning in a Bayesian setting. We first analyze two commonly used greedy active learning criteria: the maximum entropy criterion, which selects the example with the highest entropy, and the least confidence criterion, which selects the example whose most probable label has the least probability value. We show that(More)
We introduce a new objective function for pool-based Bayesian active learning with probabilistic hypotheses. This objective function, called the policy Gibbs error, is the expected error rate of a random classifier drawn from the prior distribution on the examples adaptively selected by the active learning policy. Exact maximization of the policy Gibbs(More)
Congenital insensitivity to pain with anhidrosis (CIPA) is a rare inherited disorder of the peripheral nervous system resulting from mutations in neurotrophic tyrosine kinase receptor 1 gene (NTRK1), which encodes the high-affinity nerve growth factor receptor TRKA. Here, we investigated the oral and craniofacial manifestations of a Chinese patient affected(More)
Dependencies among neighboring labels in a sequence are important sources of information for sequence labeling and segmentation. However, only first-order dependencies, which are dependencies between adjacent labels or segments, are commonly exploited in practice because of the high computational complexity of typical inference algorithms when longer(More)