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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 852 (24 self)
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probability is able to output an hypothesis that is correct on all but an arbitrarily small fraction of the instances. The concept class is weakly learnable if the learner can produce an hypothesis that performs only slightly better than random guessing. In this paper, it is shown that these two notions
On the equivalence of weak learnability and linear separability: New relaxations and efficient boosting algorithms
 IN: PROCEEDINGS OF THE 21ST ANNUAL CONFERENCE ON COMPUTATIONAL LEARNING THEORY
"... Boosting algorithms build highly accurate prediction mechanisms from a collection of lowaccuracy predictors. To do so, they employ the notion of weaklearnability. The starting point of this paper is a proof which shows that weak learnability is equivalent to linear separability with ℓ1 margin. Whil ..."
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Cited by 33 (7 self)
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Boosting algorithms build highly accurate prediction mechanisms from a collection of lowaccuracy predictors. To do so, they employ the notion of weaklearnability. The starting point of this paper is a proof which shows that weak learnability is equivalent to linear separability with ℓ1 margin
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 509 (15 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
On the Learnability
"... This paper was selected by a process of anonymous peer reviewing for presentation at ..."
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This paper was selected by a process of anonymous peer reviewing for presentation at
Learnability
"... LEARNABILITY. The mathematical theory of language learnability (also known as learnability theory, grammar induction, or grammatical inference) deals with idealized “learning procedures ” for acquiring grammars on the basis of exposure to evidence about languages. In one classic paradigm, presented ..."
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LEARNABILITY. The mathematical theory of language learnability (also known as learnability theory, grammar induction, or grammatical inference) deals with idealized “learning procedures ” for acquiring grammars on the basis of exposure to evidence about languages. In one classic paradigm, presented
“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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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 problem of detecting the period in which information diffusion burst occurs from a single observed diffusion sequence under the assumption that the delay of the information propagation over a social network follows the exponential distribution. To be more precise, we formulated the problem of detecting the change points and finding the values of the time delay parameter in the exponential distribution as an optimization problem of maximizing the likelihood of generating the observed diffusion sequence. We devised an efficient iterative search algorithm for the change point detection whose time complexity is almost linear to the number of data points. We tested the algorithm against the real Twitter data of the 2011 Tohoku earthquake and tsunami, and experimentally confirmed that the algorithm is much more efficient than the exhaustive naive search and is much more accurate than the simple greedy search. 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
The learnability of metrical phonology
, 2007
"... ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof.mr. P.F. van der Heijden ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Aula der Universiteit op dinsdag 9 januari 2007, t ..."
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Cited by 42 (5 self)
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ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof.mr. P.F. van der Heijden ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Aula der Universiteit op dinsdag 9 januari 2007, te 14.00 uur
Results 1  10
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