We study the problem of combining active learning suggestions to identify informative training examples by empirically comparing methods on benchmark datasets. Many active learning heuristics for classification problems have been proposed to help us pick which instance to annotate next. But what is the optimal heuristic for a particular source of data? Motivated by the success of methods that combine predictors, we combine active learners with bandit algorithms and rank aggregation methods. We demonstrate that a combination of active learners outperforms passive learning in large benchmark datasets and removes the need to pick a particular active learner a priori. We discuss challenges to finding good rewards for bandit approaches and show ...
Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensiv...
Abstract We discuss a new paradigm for supervised learning that aims at improving the efficiency of ...
In this work we proposed a novel transductive method to solve the problem of learning from partially...
We study the problem of combining active learning suggestions to identify informative training examp...
Pool-based active learning is an important technique that helps reduce labeling efforts within a poo...
Hasenjäger M. Active data selection in supervised and unsupervised learning. Bielefeld: Bielefeld Un...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
Active machine learning algorithms are used when large numbers of unlabeled examples are available a...
International audienceIn the context of Active Learning for classification, the classification error...
Object classification by learning from data is a vast area of statistics and machine learning. Withi...
International audienceIn this paper, we propose to reformulate the active learning problem occurring...
Abstract—In this paper, we propose to reformulate the active learning problem occurring in classific...
In many real world supervised learning problems, it is easy or cheap to acquire unlabelled data, but...
21st International Conference on Neural Information ProcessingThe labelling of training examples is ...
Statistical analysis and pattern recognition have become a daunting endeavour in face of the enormou...
Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensiv...
Abstract We discuss a new paradigm for supervised learning that aims at improving the efficiency of ...
In this work we proposed a novel transductive method to solve the problem of learning from partially...
We study the problem of combining active learning suggestions to identify informative training examp...
Pool-based active learning is an important technique that helps reduce labeling efforts within a poo...
Hasenjäger M. Active data selection in supervised and unsupervised learning. Bielefeld: Bielefeld Un...
In recent decades, the availability of a large amount of data has propelled the field of machine lea...
Active machine learning algorithms are used when large numbers of unlabeled examples are available a...
International audienceIn the context of Active Learning for classification, the classification error...
Object classification by learning from data is a vast area of statistics and machine learning. Withi...
International audienceIn this paper, we propose to reformulate the active learning problem occurring...
Abstract—In this paper, we propose to reformulate the active learning problem occurring in classific...
In many real world supervised learning problems, it is easy or cheap to acquire unlabelled data, but...
21st International Conference on Neural Information ProcessingThe labelling of training examples is ...
Statistical analysis and pattern recognition have become a daunting endeavour in face of the enormou...
Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensiv...
Abstract We discuss a new paradigm for supervised learning that aims at improving the efficiency of ...
In this work we proposed a novel transductive method to solve the problem of learning from partially...