On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

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• An overview of statistical learning theory→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Large-Scale Machine Learning with Stochastic Gradient Descent→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Semi-Supervised Learning→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Boosting the margin: a new explanation for the effectiveness of voting methods→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Rademacher and Gaussian Complexities: Risk Bounds and Structural Results→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• The Boosting Approach to Machine Learning: An Overview→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Stochastic Gradient Descent Tricks→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Learnability and the Vapnik-Chervonenkis dimension→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Kernel methods in machine learning→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm→On the Uniform Convergence of Relative Frequencies of Events to Their ProbabilitiesLegacy edge — source detail unavailable
• Semi-Supervised Learning→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
• Semi-Supervised Learning→Rademacher and Gaussian Complexities: Risk Bounds and Structural ResultsLegacy 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
• An overview of statistical learning theory→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→Learnability and the Vapnik-Chervonenkis dimensionLegacy edge — source detail unavailable
• Rademacher and Gaussian Complexities: Risk Bounds and Structural Results→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Large-Scale Machine Learning with Stochastic Gradient DescentLegacy edge — source detail unavailable