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830
Learnability and the VapnikChervonenkis dimension
, 1989
"... Valiant’s learnability model is extended to learning classes of concepts defined by regions in Euclidean space E”. The methods in this paper lead to a unified treatment of some of Valiant’s results, along with previous results on distributionfree convergence of certain pattern recognition algorith ..."
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Cited by 727 (22 self)
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Valiant’s learnability model is extended to learning classes of concepts defined by regions in Euclidean space E”. The methods in this paper lead to a unified treatment of some of Valiant’s results, along with previous results on distributionfree convergence of certain pattern recognition
The strength of weak learnability
 MACHINE LEARNING
, 1990
"... This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high prob ..."
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Cited by 871 (26 self)
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This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (16 self)
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presented by Schapire in his paper "The strength of weak learnability", and represents an improvement over his results. The analysis of our algorithm provides general upper bounds on the resources required for learning in Valiant's polynomial PAC learning framework, which are the best general
Scalesensitive Dimensions, Uniform Convergence, and Learnability
, 1997
"... Learnability in Valiant's PAC learning model has been shown to be strongly related to the existence of uniform laws of large numbers. These laws define a distributionfree convergence property of means to expectations uniformly over classes of random variables. Classes of realvalued functions ..."
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Cited by 242 (2 self)
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Learnability in Valiant's PAC learning model has been shown to be strongly related to the existence of uniform laws of large numbers. These laws define a distributionfree convergence property of means to expectations uniformly over classes of random variables. Classes of realvalued functions
Learning Decision Lists
, 2001
"... This paper introduces a new representation for Boolean functions, called decision lists, and shows that they are efficiently learnable from examples. More precisely, this result is established for \kDL" { the set of decision lists with conjunctive clauses of size k at each decision. Since k ..."
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Cited by 427 (0 self)
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strictly increases the set of functions which are known to be polynomially learnable, in the sense of Valiant (1984). Our proof is constructive: we present an algorithm which can efficiently construct an element of kDL consistent with a given set of examples, if one exists.
Efficient noisetolerant learning from statistical queries
 JOURNAL OF THE ACM
, 1998
"... In this paper, we study the problem of learning in the presence of classification noise in the probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” learning algorithms in the most general way, we formalize a new but related model of learning from stat ..."
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Cited by 353 (5 self)
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of functions learnable from statistical queries is in fact learnable with classification noise in Valiant’s model, with a noise rate approaching the informationtheoretic barrier of 1/2. We then demonstrate the generality of the statistical query model, showing that practically every class learnable in Valiant
Leslie Valiant Harvard University
"... Abstract Recently, a new formal model of learnability was introduced [23]. The model is applicable to practical learning systems because it requires the learning algorithm to be feasibly computable, yet at the same time demands only that the algorithm find an approximation to the unknown rule. We su ..."
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Abstract Recently, a new formal model of learnability was introduced [23]. The model is applicable to practical learning systems because it requires the learning algorithm to be feasibly computable, yet at the same time demands only that the algorithm find an approximation to the unknown rule. We
On the Learnability of the Uncomputable
, 1996
"... Within Valiant's model of learning as formalized by Kearns, we show that computable total predicates for two formally uncomputable problems (the classical Halting Problem, and the Halting Problem relative to a specified oracle) are formally learnable from examples, to arbitrarily high acc ..."
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Cited by 1 (0 self)
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Within Valiant's model of learning as formalized by Kearns, we show that computable total predicates for two formally uncomputable problems (the classical Halting Problem, and the Halting Problem relative to a specified oracle) are formally learnable from examples, to arbitrarily high
Learnability of Description Logics
 IN PROCEEDINGS OF THE FOURTH ANNUAL WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... This paper considers the learnability of subsets of firstorder logic. Prior work has established two boundaries of learnability: Haussler [ 1989 ] has shown that conjunctions in firstorder logic cannot be learned in the Valiant model, even if the form of the conjunction is highly restricted; ..."
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Cited by 10 (5 self)
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This paper considers the learnability of subsets of firstorder logic. Prior work has established two boundaries of learnability: Haussler [ 1989 ] has shown that conjunctions in firstorder logic cannot be learned in the Valiant model, even if the form of the conjunction is highly restricted
“Learnable
"... Abstract: Short summary of most important research results that explain why the work was done, what was accomplished, and how it pushed scientific frontiers or advanced the field. This summary will be used for archival purposes and will be added to a searchable DoD database. First, we addressed the ..."
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. Second, we addressed the problem of how people make their own decisions based on their neighbors ’ opinions. The model best suited to discuss this problem is the voter model and several
Results 1  10
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830