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Learning and Generalization in a Two–Layer Neural Network: The Role of the Vapnik–Chervonenkis–Dimension

Opper, M.

Phys. Rev. Lett., 2113–2116. 1994

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Phase Transition and $1/f$ Noise in a Game dynamical Model

Opper, M. and Diederich, S.

Phys. Rev. Lett., 1616-1619. 1992

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New Method for Studying the Dynamics of Disordered Spin Systems without Finite–Size Effects

Eissfeller, H. and Opper, M.

Phys. Rev. Lett., 2094–2097. 1992

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Generalization Performance of Bayes Optimal Prediction Algorithm for Learning a Perceptron

Opper, M. and Haussler, D.

Phys. Rev. Lett., 2677–2680. 1991

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Learning of Correlated Patterns in Spin- Glass Networks by Local Learning Rules

Diederich, S. and Opper, M.

Phys. Rev. Lett., 949–952. 1987

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Efficient Statistical Inference for Stochastic Reaction Processes

Ruttor, A. and Opper, M.

Phys. Rev. Lett., 230601. 2009

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Statistical Mechanics of Learning: A Variational Approach for Real Data

Malzahn, D. and Opper, M.

Phys. Rev. Lett., 108302.1-108302.4. 2002

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Tractable approximations for probabilistic models: The adaptive TAP mean field approach

Opper, M. and Winther, O.

Phys. Rev. Lett., 3695-3699. 2001

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Universal learning curves of support vector machines

Opper, M. and Urbanczik, R.

Phys. Rev. Lett., 4410-4413. 2001

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Retarded Learning: Rigorous Results from Statistical Mechanics

Herschkowitz, D. and Opper, M.

Phys. Rev. Lett., 2174-2177. 2001

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Statistical Mechanics of Support Vector Networks

Dietrich, R., Opper, M. and Sompolinsky, H.

Phys. Rev. Lett., 2975-2978. 1999

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Online versus Offline Learning from Random Examples: General Results

Opper, M.

Phys. Rev. Lett., 4671-4674. 1996

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A mean field approach to Bayes learning in feed–forward neural networks

Opper, M. and Winther, O.

Phys. Rev. Lett., 1964-1967. 1996

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A Mean Field Monte Carlo Approach to the Sherrington–Kirkpatrick Model with Asymmetric Couplings

Eissfeller, H. and Opper, M.

Phys. Rev. E, 709–720. 1994

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Neural cryptography with feedback

Ruttor, A., Kinzel, W., Shacham, L. and Kanter, I.

Phys. Rev. E, 046110. 2004

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Learning and Generalization in a Two–Layer Neural Network: The Role of the Vapnik–Chervonenkis–Dimension

Phase Transition and $1/f$ Noise in a Game dynamical Model

New Method for Studying the Dynamics of Disordered Spin Systems without Finite–Size Effects

Generalization Performance of Bayes Optimal Prediction Algorithm for Learning a Perceptron

Learning of Correlated Patterns in Spin- Glass Networks by Local Learning Rules

Efficient Statistical Inference for Stochastic Reaction Processes

Statistical Mechanics of Learning: A Variational Approach for Real Data

Tractable approximations for probabilistic models: The adaptive TAP mean field approach

Universal learning curves of support vector machines

Retarded Learning: Rigorous Results from Statistical Mechanics

Statistical Mechanics of Support Vector Networks

Online versus Offline Learning from Random Examples: General Results

A mean field approach to Bayes learning in feed–forward neural networks

A Mean Field Monte Carlo Approach to the Sherrington–Kirkpatrick Model with Asymmetric Couplings

Neural cryptography with feedback

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