Material Detail

Active Learning

Active Learning

This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. Active learning is defined by contrast to the passive model of supervised learning where all the labels for learning are obtained without reference to the learning algorithm, while in active learning the learner interactively chooses which data points to label. The hope of active learning is that interaction can substantially reduce the number of labels required, making solving problems via machine learning more practical. This hope is known to be valid in certain special cases, both empirically and theoretically. Variants of active learning have been investigated over several decades and fields. The focus of this tutorial is on general techniques which are applicable to many problems. At a... Show More
Rate

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.
hidden