Bayesian Program Learning and Concept Induction
Abstract
Tenenbaum proposes to investigate and formalize an approach to learning and concept formation that will provide measureable results indicating significant reduction in costs for a system to learn and formulate new concepts. His work will focus on Bayesian inference to perform learning and concept induction. Many machine learning approaches require large populations of learning examples as training sets used to train learning algorithms. Tenenbaum asks the question, why do supervised learning algorithms require such large numbers of learning examples when in contrast, children often quickly learn a new concept with just one learning example.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 23, 2016
- Source ID
- FA95501610012
Entities
People
- Joshua B. Tenenbaum
Organizations
- Air Force Office of Scientific Research
- Harvard University
- United States Air Force