Active learning - Approaches and issues

Tirthankar RayChaudhuri, Leonard G C Hamey

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This paper surveys published work in active learning research with the purpose of providing a unified understanding of the area. A passive learning system relies entirely on pre-gathered information, whereas an active learning algorithm has the capability of interacting with its environment in order to collect information and/or to select learning policy. Active learning systems produce improved generalisation, reduce data costs and are most useful where data is expensive and computation is cheap. There are three major recognised approaches to the implementation of active learning - goal-driven learning, reinforcement learning and querying. While the first is largely a meta-level symbolic approach, the second is more a class of problems employing a policy-based approach to learning in non-deterministic dynamic environments; the third is based upon gathering the most useful examples by asking 'intelligent' questions. Research in the area is still mostly at a theoretical level.

Original languageEnglish
Pages (from-to)205-243
Number of pages39
JournalJournal of Intelligent Systems
Issue number3-4
Publication statusPublished - 1997


  • Active Learning
  • Exploration and Exploitation
  • Goal-Driven Learning
  • Markov Decision Processes
  • Optimum Experiment Design
  • Query Filtering
  • Reinforcement Learning


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