What Size Net Gives Valid Generalization?

Citation edge evidence

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• Deep learning in neural networks: An overview→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• A training algorithm for optimal margin classifiers→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Artificial neural networks: fundamentals, computing, design, and application→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• A Short Introduction to Boosting→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Optimal Brain Damage→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Improved boosting algorithms using confidence-rated predictions→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Boosting the margin: a new explanation for the effectiveness of voting methods→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• The Boosting Approach to Machine Learning: An Overview→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Improved Boosting Algorithms Using Confidence-rated Predictions→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Fundamentals of Artificial Neural Networks→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Learnability and the Vapnik-Chervonenkis dimension→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Pruning algorithms-a survey→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Neural networks for classification: a survey→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• What Size Net Gives Valid Generalization?→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
• What Size Net Gives Valid Generalization?→Parallel Networks that Learn to Pronounce English TextLegacy edge — source detail unavailable
• What Size Net Gives Valid Generalization?→Connectionist learning proceduresLegacy edge — source detail unavailable
• What Size Net Gives Valid Generalization?→Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold AlgorithmLegacy edge — source detail unavailable
• Boosting the margin: a new explanation for the effectiveness of voting methods→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
• Boosting the margin: a new explanation for the effectiveness of voting methods→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
• Artificial neural networks: fundamentals, computing, design, and application→30 years of adaptive neural networks: perceptron, Madaline, and backpropagationLegacy edge — source detail unavailable
• Artificial neural networks: fundamentals, computing, design, and application→Fundamentals of Artificial Neural NetworksLegacy edge — source detail unavailable
• Improved boosting algorithms using confidence-rated predictions→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Optimal Brain DamageLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Connectionist learning proceduresLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• A training algorithm for optimal margin classifiers→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→Parallel Networks that Learn to Pronounce English TextLegacy edge — source detail unavailable
• 30 years of adaptive neural networks: perceptron, Madaline, and backpropagation→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Optimal Brain DamageLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Pruning algorithms-a surveyLegacy edge — source detail unavailable
• A Short Introduction to Boosting→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
• A Short Introduction to Boosting→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
• A Short Introduction to Boosting→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
• A Short Introduction to Boosting→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
• Pruning algorithms-a survey→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
• Learnability and the Vapnik-Chervonenkis dimension→Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold AlgorithmLegacy edge — source detail unavailable
• The Boosting Approach to Machine Learning: An Overview→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
• The Boosting Approach to Machine Learning: An Overview→Improved boosting algorithms using confidence-rated predictionsLegacy edge — source detail unavailable
• The Boosting Approach to Machine Learning: An Overview→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
• The Boosting Approach to Machine Learning: An Overview→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
• The Boosting Approach to Machine Learning: An Overview→Improved Boosting Algorithms Using Confidence-rated PredictionsLegacy edge — source detail unavailable
• Connectionist learning procedures→Parallel Networks that Learn to Pronounce English TextLegacy edge — source detail unavailable
• Improved Boosting Algorithms Using Confidence-rated Predictions→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable