Abstract
The computational complexity of learning Boolean concepts from examples is investigated. It is shown for various classes of concept representations that these cannot be learned feasibly in a distribution-free sense unless R = NP. These classes include (a) disjunctions of two monomials, (b) Boolean threshold functions, and (c) Boolean formulas in which each variable occurs at most once. Relationships between learning of heuristics and finding approximate solutions to NP-hard optimization problems are given.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 965-984 |
| Number of pages | 20 |
| Journal | Journal of the ACM (JACM) |
| Volume | 35 |
| Issue number | 4 |
| DOIs | |
| State | Published - Oct 1 1988 |
| Externally published | Yes |
Keywords
- Distribution-free learning
- NPcompleteness
- inductive inference
- learnability
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Information Systems
- Hardware and Architecture
- Artificial Intelligence