Monotone term decision lists
TLDR
A new representation class of Boolean functions, monotone term decision lists, is introduced which combines compact representation size with tractability of essential operations and is an attractive alternative to traditional universal representation classes such as DNF formulas or decision trees.About:
This article is published in Theoretical Computer Science.The article was published on 2001-05-28 and is currently open access. It has received 10 citations till now. The article focuses on the topics: Decision list & Monotone polygon.read more
Citations
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
On online learning of decision lists
Ziv Nevo,Ran El-Yaniv +1 more
TL;DR: A novel online algorithm is presented that achieves a mistake bound of O(rDlog n), where r is the number of relevant variables, and n is the total number of variables.
DissertationDOI
Learning Comprehensible Theories from Structured Data
TL;DR: This thesis is concerned with the problem of learning comprehensible theories from structured data and covers primarily classification and regression learning and the usefulness of the learning system developed is demontrated with applications in two important domains: bioinformatics and intelligent agents.
Decision lists and threshold decision lists
TL;DR: The key areas explored are the representation of Boolean functions by decision lists and threshold decision lists; properties of classes of decision list; and algorithmic questions associated with decision lists.
References
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Graph-Based Algorithms for Boolean Function Manipulation
TL;DR: In this paper, the authors present a data structure for representing Boolean functions and an associated set of manipulation algorithms, which have time complexity proportional to the sizes of the graphs being operated on, and hence are quite efficient as long as the graphs do not grow too large.
Proceedings ArticleDOI
A theory of the learnable
TL;DR: This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.
Journal ArticleDOI
Queries and Concept Learning
TL;DR: This work considers the problem of using queries to learn an unknown concept, and several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries.
Book
An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
TL;DR: The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata is described.