Add to ORKGA common belief in unbiased active learning is that, in order to capture the most informative instances, the sampling probabilities should be proportional to the uncertainty of the class labels. We argue that this produces suboptimal predictions and present sampling schemes for unbiased pool-based active learning that minimise the actual prediction error, and demonstrate a better predictive performance than competing methods on a number of benchmark datasets. In contrast, both probabilistic and deterministic uncertainty sampling performed worse than simple random sampling on some of the datasets
Abstract We address problems of model misspeci\u85cation in active learning. We suppose that an inve...
Which active learning methods can we expect to yield good performance in learning binary and multi-c...
This thesis addresses a problem arising in large and expensive experiments where incomplete data com...
Active learning provides promising methods to optimize the cost of manually annotating a dataset. Ho...
AbstractWe state and analyze the first active learning algorithm that finds an ϵ-optimal hypothesis ...
For many supervised learning tasks, the cost of acquiring training data is dominated by the cost of ...
Abstract. In many cost-sensitive environments class probability estimates are used by decision maker...
International audienceIn the context of Active Learning for classification, the classification error...
We introduce a method of Robust Learning (‘robl’) for binary data, and propose its use in situations...
Models that can actively seek out the best quality training data hold the promise of more accurate, ...
This thesis presents a general discussion of active learning and adaptive sampling. In many practica...
Data subsampling has become widely recognized as a tool to overcome computational and economic bottl...
Machine learning models are often trained on data sets subject to selection bias. In particular, sel...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
Object classification by learning from data is a vast area of statistics and machine learning. Withi...
Abstract We address problems of model misspeci\u85cation in active learning. We suppose that an inve...
Which active learning methods can we expect to yield good performance in learning binary and multi-c...
This thesis addresses a problem arising in large and expensive experiments where incomplete data com...
Active learning provides promising methods to optimize the cost of manually annotating a dataset. Ho...
AbstractWe state and analyze the first active learning algorithm that finds an ϵ-optimal hypothesis ...
For many supervised learning tasks, the cost of acquiring training data is dominated by the cost of ...
Abstract. In many cost-sensitive environments class probability estimates are used by decision maker...
International audienceIn the context of Active Learning for classification, the classification error...
We introduce a method of Robust Learning (‘robl’) for binary data, and propose its use in situations...
Models that can actively seek out the best quality training data hold the promise of more accurate, ...
This thesis presents a general discussion of active learning and adaptive sampling. In many practica...
Data subsampling has become widely recognized as a tool to overcome computational and economic bottl...
Machine learning models are often trained on data sets subject to selection bias. In particular, sel...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
Object classification by learning from data is a vast area of statistics and machine learning. Withi...
Abstract We address problems of model misspeci\u85cation in active learning. We suppose that an inve...
Which active learning methods can we expect to yield good performance in learning binary and multi-c...
This thesis addresses a problem arising in large and expensive experiments where incomplete data com...