On the equivalence of Occam algorithms

Blumer et al. (1987, 1989) showed that any concept class that is learnable by Occam algorithms is PAC learnable. Board and Pitt (1990) showed a partial converse of this theorem: for concept classes that are closed under exception lists, any class that is PAC learnable is learnable by an Occam algorithm. However, their Occam algorithm outputs a hypothesis whose complexity is δ -dependent, which is an important limitation. In this paper, we show that their partial converse applies to Occam algorithms with δ -independent complexities as well. Thus, we provide a posteriori justification of various theoretical results and algorithm design methods which use the partial converse as a basis for their work.

Institutions

Publication Details

Journal
Information and Computation
Published
2025-10-24
DOI
https://doi.org/10.1016/j.ic.2025.105370
Primary Topic
Machine Learning and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

On the equivalence of Occam algorithms

Information and Computation
Machine Learning and Algorithms
article

On the equivalence of Occam algorithms

article en

Abstract

Blumer et al. (1987, 1989) showed that any concept class that is learnable by Occam algorithms is PAC learnable. Board and Pitt (1990) showed a partial converse of this theorem: for concept classes that are closed under exception lists, any class that is PAC learnable is learnable by an Occam algorithm. However, their Occam algorithm outputs a hypothesis whose complexity is δ -dependent, which is an important limitation. In this paper, we show that their partial converse applies to Occam algorithms with δ -independent complexities as well. Thus, we provide a posteriori justification of various theoretical results and algorithm design methods which use the partial converse as a basis for their work.

Information and ComputationVol. 307
Center for Theoretical Physics (PL), University of Oxford (GB)
Quality Education
Openalex Percentile: Top 23%
Machine Learning and Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.