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Exponentiated Gradient Versus Gradient Descent for Linear Predictors
 Information and Computation
, 1995
"... this paper, we concentrate on linear predictors . To any vector u 2 R ..."
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Cited by 247 (12 self)
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this paper, we concentrate on linear predictors . To any vector u 2 R
On Weak Learning
 Journal of Computer and System Sciences
, 1995
"... This paper presents relationships between weak learning, weak prediction (where the probability of being correct is slightly larger than 50%), and consistency oracles (which decide whether or not a given set of examples is consistent with a concept in the class). Our main result is a simple polynomi ..."
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Cited by 50 (9 self)
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This paper presents relationships between weak learning, weak prediction (where the probability of being correct is slightly larger than 50%), and consistency oracles (which decide whether or not a given set of examples is consistent with a concept in the class). Our main result is a simple polynomial prediction algorithm which makes only a single query to a consistency oracle and whose predictions have a polynomial edge over random guessing. We compare this prediction algorithm with several of the standard prediction techniques, deriving an improved worst case bound on Gibbs Algorithm in the process. We use our algorithm to show that a concept class is polynomially learnable if and only if there is a polynomial probabilistic consistency oracle for the class. Since strong learning algorithms can be built from weak learning algorithms, our results also characterizes strong learnability.
Polynomial Learnability and Inductive Logic Programming: Methods and Results
 New Generation Computing
, 1995
"... Over the last few years, the ecient learnability of logic programs has been studied extensively. Positive and negative learnability results now exist for a number of restricted classes of logic programs that are closely related to the classes used in practice within inductive logic programming. T ..."
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Cited by 24 (1 self)
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Over the last few years, the ecient learnability of logic programs has been studied extensively. Positive and negative learnability results now exist for a number of restricted classes of logic programs that are closely related to the classes used in practice within inductive logic programming. This paper surveys these results, and also introduces some of the more useful techniques for deriving such results. The paper does not assume any prior background in computational learning theory.
Probabilistic Analysis of Learning in Artificial Neural Networks: The PAC Model and its Variants
, 1997
"... There are a number of mathematical approaches to the study of learning and generalization in artificial neural networks. Here we survey the `probably approximately correct' (PAC) model of learning and some of its variants. These models provide a probabilistic framework for the discussion of gen ..."
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Cited by 19 (4 self)
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There are a number of mathematical approaches to the study of learning and generalization in artificial neural networks. Here we survey the `probably approximately correct' (PAC) model of learning and some of its variants. These models provide a probabilistic framework for the discussion of generalization and learning. This survey concentrates on the sample complexity questions in these models; that is, the emphasis is on how many examples should be used for training. Computational complexity considerations are briefly discussed for the basic PAC model. Throughout, the importance of the VapnikChervonenkis dimension is highlighted. Particular attention is devoted to describing how the probabilistic models apply in the context of neural network learning, both for networks with binaryvalued output and for networks with realvalued output.