Add to ORKGWe characterize the sample complexity of active learning problems in terms of a parameter which takes into account the distribution over the input space, the specific target hypothesis, and the desired accuracy
The original and most widely studied PAC model for learning assumes a passive learner in the sense t...
Active learning (AL) is a branch of machine learning that deals with problems where unlabeled data i...
We present a simple noise-robust margin-based active learn-ing algorithm to find homogeneous (passin...
This paper analyzes the potential advantages and theoretical challenges of active learning algorit...
The sample complexity of active learning under the realizability assumption has been well-studied. T...
The sample complexity of active learning under the realizability assumption has been well-studied. T...
We study pool-based active learning in the presence of noise, that is, the agnostic setting. It is k...
Sequential algorithms of active learning based on the estimation of the level sets of the empirical ...
This dissertation develops and analyzes active learning algorithms for binary classification problem...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
AbstractWe state and analyze the first active learning algorithm that finds an ϵ-optimal hypothesis ...
We discuss basic sample complexity theory and it's impact on classification success evaluation,...
We present an agnostic active learning algorithm for any hypothesis class of bounded VC dimension un...
International audienceActive learning is a branch of Machine Learning in which the learning algorith...
We present a simple noise-robust margin-based active learn-ing algorithm to find homogeneous (passin...
The original and most widely studied PAC model for learning assumes a passive learner in the sense t...
Active learning (AL) is a branch of machine learning that deals with problems where unlabeled data i...
We present a simple noise-robust margin-based active learn-ing algorithm to find homogeneous (passin...
This paper analyzes the potential advantages and theoretical challenges of active learning algorit...
The sample complexity of active learning under the realizability assumption has been well-studied. T...
The sample complexity of active learning under the realizability assumption has been well-studied. T...
We study pool-based active learning in the presence of noise, that is, the agnostic setting. It is k...
Sequential algorithms of active learning based on the estimation of the level sets of the empirical ...
This dissertation develops and analyzes active learning algorithms for binary classification problem...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
AbstractWe state and analyze the first active learning algorithm that finds an ϵ-optimal hypothesis ...
We discuss basic sample complexity theory and it's impact on classification success evaluation,...
We present an agnostic active learning algorithm for any hypothesis class of bounded VC dimension un...
International audienceActive learning is a branch of Machine Learning in which the learning algorith...
We present a simple noise-robust margin-based active learn-ing algorithm to find homogeneous (passin...
The original and most widely studied PAC model for learning assumes a passive learner in the sense t...
Active learning (AL) is a branch of machine learning that deals with problems where unlabeled data i...
We present a simple noise-robust margin-based active learn-ing algorithm to find homogeneous (passin...