Results 1  10
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596
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
Combining labeled and unlabeled data with cotraining
, 1998
"... We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the ta ..."
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Cited by 1633 (28 self)
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algorithm's predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PACstyle analysis for this setting, and, more broadly, a PACstyle framework for the general problem of learning from both labeled and unlabeled data. We also
Cryptographic Limitations on Learning Boolean Formulae and Finite Automata
 PROCEEDINGS OF THE TWENTYFIRST ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING
, 1989
"... In this paper we prove the intractability of learning several classes of Boolean functions in the distributionfree model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are representation independent, in that they hold regardless of the syntact ..."
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Cited by 347 (14 self)
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In this paper we prove the intractability of learning several classes of Boolean functions in the distributionfree model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results are representation independent, in that they hold regardless
Learning With Many Irrelevant Features
 In Proceedings of the Ninth National Conference on Artificial Intelligence
, 1991
"... In many domains, an appropriate inductive bias is the MINFEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This paper defines and studies this bias. First, it is shown that any learning algorithm implementing the MINFEATURES bias requires \Theta( 1 ff ..."
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Cited by 252 (4 self)
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ffl ln 1 ffi + 1 ffl [2 p + p ln n]) training examples to guarantee PAClearning a concept having p relevant features out of n available features. This bound is only logarithmic in the number of irrelevant features. The paper also presents a quasipolynomial time algorithm, FOCUS, which
PAC Associative Reinforcement Learning
, 1995
"... General algorithms for the reinforcement learning problem typically learn policies in the form of a table that directly maps the states of the environment into actions. When the statespace is large these methods become impractical. One approach to increase efficiency is to restrict the class of pol ..."
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Cited by 1 (0 self)
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of policies by considering only policies that can be described using some fixed representation. This paper pursues this approach and analyzes the associative reinforcement learning problem in the PAC learning framework. As a representation, we use a general form of decision lists that can describe a wide
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
Reinforcement Learning in Finite MDPs: PAC Analysis Reinforcement Learning in Finite MDPs: PAC Analysis
"... Editor: We study the problem of learning nearoptimal behavior in finite Markov Decision Processes (MDPs) with a polynomial number of samples. These “PACMDP ” algorithms include the wellknown E 3 and RMAX algorithms as well as the more recent Delayed Qlearning algorithm. We summarize the current ..."
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Cited by 52 (6 self)
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Editor: We study the problem of learning nearoptimal behavior in finite Markov Decision Processes (MDPs) with a polynomial number of samples. These “PACMDP ” algorithms include the wellknown E 3 and RMAX algorithms as well as the more recent Delayed Qlearning algorithm. We summarize
Extension of the PAC Framework to Finite and Countable Markov Chains
 In Proceedings of the 12th Annual Conference on Computational Learning Theory
, 2000
"... We consider a model of learning in which the successive observations follow a certain Markov chain. The observations are labeled according to a membership to some unknown target set. For a Markov chain with finitely many states we show that, if the target set belongs to a family of sets with a finit ..."
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Cited by 24 (1 self)
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finite VC dimension, then probably approximately correct learning of this set is possible with polynomially large samples. Specifically for observations following a random walk with a state space X and uniform stationary distribution, the sample size required is no more than\Omega i t 0 1\Gamma 2 log
An equivalence between sparse approximation and Support Vector Machines
 A.I. Memo 1606, MIT Arti cial Intelligence Laboratory
, 1997
"... This publication can be retrieved by anonymous ftp to publications.ai.mit.edu. The pathname for this publication is: aipublications/15001999/AIM1606.ps.Z This paper shows a relationship between two di erent approximation techniques: the Support Vector Machines (SVM), proposed by V.Vapnik (1995), ..."
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Cited by 243 (7 self)
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programming problem. In the appendix we also present a derivation of the SVM technique in the framework of regularization theory, rather than statistical learning theory, establishing a connection between SVM, sparse approximation and regularization theory.
First order jkclausal theories are PAClearnable
 Artificial Intelligence
, 1994
"... We present positive PAClearning results for the nonmonotonic inductive logic programming setting. In particular, we show that first order rangerestricted clausal theories that consist of clauses with up to k literals of size at most j each are polynomialsample polynomialtime PAClearnable with on ..."
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Cited by 71 (26 self)
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We present positive PAClearning results for the nonmonotonic inductive logic programming setting. In particular, we show that first order rangerestricted clausal theories that consist of clauses with up to k literals of size at most j each are polynomialsample polynomialtime PAC
Results 1  10
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596