Communication Dans Un Congrès Année : 2010

Complexity Bounds for Batch Active Learning in Classification

Résumé

Active learning is a branch of Machine Learning in which the learning algorithm, instead of being directly provided with pairs of problem instances and their solutions (their labels), is allowed to choose, from a set of unlabeled data, which instances to query. It is suited to settings where labeling instances is costly. This paper analyzes the speed-up of batch (parallel) active learning compared to sequential active learning (where instances are chosen 1 by 1): how faster can an algorithm become if it can query instances at once? There are two main contributions: proving lower and upper bounds on the possible gain, and illustrating them by experimenting on usual active learning algorithms. Roughly speaking, the speed-up is asymptotically logarithmic in the batch size (i.e. when ! 1). However, for some classes of functions with finite VC-dimension V , a linear speed-up can be achieved until a batch size of V . Practically speaking, this means that parallelizing computations on an expensive-to-label problem which is suited to active learning is very beneficial until V simultaneous queries, and less interesting (yet still bringing improvement) afterwards.

Fichier principal
Vignette du fichier
batchal.pdf (158.41 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence
Loading...

Dates et versions

inria-00533318 , version 1 (05-11-2010)

Licence

Identifiants

  • HAL Id : inria-00533318 , version 1

Citer

Philippe Rolet, Olivier Teytaud. Complexity Bounds for Batch Active Learning in Classification. ECML 2010, Oct 2010, Barcelone, Spain. ⟨inria-00533318⟩
284 Consultations
410 Téléchargements

Partager

  • More