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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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statistical queries. Intuitively, in this model, a learning algorithm is forbidden to examine individual examples of the unknown target function, but is given access to an oracle providing estimates of probabilities over the sample space of random examples. One of our main results shows that any class
Statistical Queries
"... We show that random DNF formulas, random logdepth decision trees and random deterministic finite acceptors cannot be weakly learned with a polynomial number of statistical queries with respect to an arbitrary distribution. 1 ..."
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We show that random DNF formulas, random logdepth decision trees and random deterministic finite acceptors cannot be weakly learned with a polynomial number of statistical queries with respect to an arbitrary distribution. 1
Statistical Queries and Faulty PAC Oracles
 In Proceedings of the Sixth Annual ACM Workshop on Computational Learning Theory
, 1993
"... In this paper we study learning in the PAC model of Valiant [18] in which the example oracle used for learning may be faulty in one of two ways: either by misclassifying the example or by distorting the distribution of examples. We first consider models in which examples are misclassified. Kearns [1 ..."
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Cited by 39 (6 self)
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[12] recently showed that efficient learning in a new model using statistical queries is a sufficient condition for PAC learning with classification noise. We show that efficient learning with statistical queries is sufficient for learning in the PAC model with malicious error rate proportional
Memory, communication, and statistical queries.
 In COLT,
, 2016
"... Abstract If a concept class can be represented with a certain amount of memory, can it be efficiently learned with the same amount of memory? What concepts can be efficiently learned by algorithms that extract only a few bits of information from each example? We introduce a formal framework for stu ..."
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Cited by 1 (1 self)
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for studying these questions, and investigate the relationship between the fundamental resources of memory or communication and the sample complexity of the learning task. We relate our memorybounded and communicationbounded learning models to the wellstudied statistical query model. This connection can
Bagging using Statistical Queries
"... Bagging is an ensemble method that relies on random resampling of a data set to construct models for the ensemble. When only statistics about the data are available, but no individual examples, the straightforward resampling procedure cannot be implemented. The question is then whether bagging c ..."
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Bagging is an ensemble method that relies on random resampling of a data set to construct models for the ensemble. When only statistics about the data are available, but no individual examples, the straightforward resampling procedure cannot be implemented. The question is then whether bagging
Noisetolerant learning, the parity problem, and the statistical query model
 J. ACM
"... We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise. This results in a polynomialtime algorithm for the case of parity functions that depend on only the first O(log n log log n) bits of input. This is the first known ins ..."
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Cited by 165 (2 self)
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instance of an efficient noisetolerant algorithm for a concept class that is provably not learnable in the Statistical Query model of Kearns [7]. Thus, we demonstrate that the set of problems learnable in the statistical query model is a strict subset of those problems learnable in the presence of noise
Statistical Query Learning (1993; Kearns)
"... The problem deals with learning to classify from random labeled examples in Valiant’s PAC model [30]. In the random classification noise model of Angluin and Laird [1] the label of each example given to the learning algorithm is flipped randomly and independently with some fixed probability η called ..."
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called the noise rate. Robustness to such benign form of noise is an important goal in the design of learning algorithms. Kearns defined a powerful and convenient framework for constructing noisetolerant algorithms based on statistical queries. Statistical query (SQ) learning is a natural restriction
Intelligent Generic Statistical Query Mode
"... This research presents a new generic approach for defining a new query mode which is the intelligent generic statistical query mode for any database application. It is combined with the optimized intelligent generic query mode (IGSQM) for relational database applications. Since it is generic then it ..."
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This research presents a new generic approach for defining a new query mode which is the intelligent generic statistical query mode for any database application. It is combined with the optimized intelligent generic query mode (IGSQM) for relational database applications. Since it is generic
Results 1  10
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3,534