Raymond A. Board

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4ranked-venue papers

4as first author

0since 2021 · last 1992

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 3 · 3 first-authorArtificial intelligence and machine learning · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science 1 paper		Approximation and online algorithms · 50% Graph algorithms and graph theory · 50%
Artificial intelligence 1 paper		Learning theory · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Graph algorithms and graph theory › graph layout graph bandwidth	1	1992	The Online Graph Bandwidth Problem · Inf. Comput. 1992
Approximation and online algorithms online algorithms	1	1992	The Online Graph Bandwidth Problem · Inf. Comput. 1992
Machine learning › Learning theory › PAC learning occam algorithms	1	1990	On the Necessity of Occam Algorithms · STOC 1990
Machine learning › Learning theory PAC learning	1	1990	On the Necessity of Occam Algorithms · STOC 1990

Year	Publication	Venue	Position
1992	The Online Graph Bandwidth Problem Raymond A. Board	Inf. Comput.	1
1992	On the Necessity of Occam Algorithms Raymond A. Board, Leonard Pitt	Theor. Comput. Sci.	1
1990	On the Necessity of Occam Algorithms abstract The distribution-independent model of concept learning from examples ("PAC-learning") due to Valiant [15] is investigated.It has been shown that the existence of an Occarn algorithm for a class of concepts is a sufficient condition for the PAC-learnability of that class [2, 3].(An Occam algorithm is a randomized polynomial-time algorithm that, when given as input a sample of strings of some unknown concept to be learned, outputs a small description of a concept that is consistent with the sample.)In this paper it is shown that for all concept classes satisfying a natural closure property the converse is also true; the PAC-learnability of the class implies the existence of an Occam algorithm for the class.This results in a complete combinatorial characterization of the PAC-learnability of a wide variety of concept classes. Raymond A. Board, Leonard Pitt	STOC	1
1989	Semi-Supervised Learning Raymond A. Board, Leonard Pitt	Mach. Learn.	1