Queries and Concept Learning
TLDR
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.Abstract:
We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries. Examples are given of efficient learning methods using various subsets of these queries for formal domains, including the regular languages, restricted classes of context-free languages, the pattern languages, and restricted types of prepositional formulas. Some general lower bound techniques are given. Equivalence queries are compared with Valiant's criterion of probably approximately correct identification under random sampling.read more
Citations
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Active Learning Literature Survey
TL;DR: This report provides a general introduction to active learning and a survey of the literature, including a discussion of the scenarios in which queries can be formulated, and an overview of the query strategy frameworks proposed in the literature to date.
Journal ArticleDOI
The Strength of Weak Learnability
TL;DR: In this paper, a method is described for converting a weak learning algorithm into one that achieves arbitrarily high accuracy, and it is shown that these two notions of learnability are equivalent.
Journal ArticleDOI
Active learning with statistical models
TL;DR: In this article, the optimal data selection techniques have been used with feed-forward neural networks and showed how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression.
Journal ArticleDOI
Learnability and the Vapnik-Chervonenkis dimension
TL;DR: This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.
Proceedings ArticleDOI
A sequential algorithm for training text classifiers
David D. Lewis,William A. Gale +1 more
TL;DR: In this article, an algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task, which reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
References
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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
Learning regular sets from queries and counterexamples
TL;DR: In this article, the problem of identifying an unknown regular set from examples of its members and nonmembers is addressed, where the regular set is presented by a minimaMy adequate teacher, which can answer membership queries about the set and can also test a conjecture and indicate whether it is equal to the unknown set and provide a counterexample if not.
Journal ArticleDOI
Learnability and the Vapnik-Chervonenkis dimension
TL;DR: This paper shows that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned.
Journal ArticleDOI
Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
TL;DR: This work presents one such algorithm that learns disjunctive Boolean functions, along with variants for learning other classes of Boolean functions.
Book
Algorithmic Program Debugging
TL;DR: An algorithm that can fix a bug that has been identified, and integrate it with the diagnosis algorithms to form an interactive debugging system that can debug programs that are too complex for the Model Inference System to synthesize.