In active learning, one aims to acquire labeled samples that are particularly useful for training a classifier. In sequential active learning, this sample selection is done in a one-at-a-time manner where the choice of sample t + 1 may depend on the current state of the classifier and the t labeled data points already available. In their deviation from standard random sampling, current active learning schemes typically introduce severe sampling bias. Even though this fact has been acknowledged in the more theoretical contributions covering active learning, the more popular approaches largely ignore this bias. This work empirically investigates the consequences of their actions and sets out to identify the pros and cons of this way of dealing with the problem of active learning. Even though current techniques can provide excellent approaches to learning, we conclude that they provide inconsistent solutions and therefore, in a strict sense, do not solve the problem of active learning.
Original languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR)
Place of PublicationPiscataway, NJ
Number of pages6
ISBN (Electronic)978-1-5090-4847-2
ISBN (Print)978-1-5090-4848-9
Publication statusPublished - 2016
EventICPR 2016: 23rd International Conference on Pattern Recognition - Cancún, Mexico
Duration: 4 Dec 20168 Dec 2016
Conference number: 23


ConferenceICPR 2016

    Research areas

  • Training, Logistics, Loss measurement, Standards, Learning systems, Convergence, Pattern recognition

ID: 47689195