- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- BIRS Workshop Lecture Videos /
- Equivalence query learning and the negation of the...
Open Collections
BIRS Workshop Lecture Videos
BIRS Workshop Lecture Videos
Equivalence query learning and the negation of the finite cover property Chase, Hunter
Description
There are multiple connections between model-theoretic notions of complexity and machine learning. NIP formulas correspond to PAC-learning by way of VC-dimension, and stable formulas correspond to online learning by way of Littlestone dimension, also known as Shelah's 2-rank. We explore a similar connection between formulas without the finite cover property and equivalence query learning. In equivalence query learning, a learner attempts to identify a certain set from a set system by making hypotheses and receiving counterexamples. We use the notion of (strong) consistency dimension, an analogue of the the negation of the finite cover property for set systems. We show that finite (strong) consistency dimension and finite LIttlestone dimension characterize equivalence query learning, drawing on ideas from model theory. We also discuss the role of Littlestone dimension and strong consistency dimension in algorithms.
Item Metadata
Title |
Equivalence query learning and the negation of the finite cover property
|
Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
|
Date Issued |
2018-10-16T16:12
|
Description |
There are multiple connections between model-theoretic notions of complexity and machine learning. NIP formulas correspond to PAC-learning by way of VC-dimension, and stable formulas correspond to online learning by way of Littlestone dimension, also known as Shelah's 2-rank.
We explore a similar connection between formulas without the finite cover property and equivalence query learning. In equivalence query learning, a learner attempts to identify a certain set from a set system by making hypotheses and receiving counterexamples. We use the notion of (strong) consistency dimension, an analogue of the the negation of the finite cover property for set systems. We show that finite (strong) consistency dimension and finite LIttlestone dimension characterize equivalence query learning, drawing on ideas from model theory. We also discuss the role of Littlestone dimension and strong consistency dimension in algorithms.
|
Extent |
26.0
|
Subject | |
Type | |
File Format |
video/mp4
|
Language |
eng
|
Notes |
Author affiliation: University of Illinois at Chicago
|
Series | |
Date Available |
2019-04-15
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
|
DOI |
10.14288/1.0378211
|
URI | |
Affiliation | |
Peer Review Status |
Unreviewed
|
Scholarly Level |
Graduate
|
Rights URI | |
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International