Neural Networks and the Bias/Variance Dilemma

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

• Deep learning in neural networks: An overview→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• A Tutorial on Support Vector Machines for Pattern Recognition→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• A training algorithm for optimal margin classifiers→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Extremely randomized trees→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Wrappers for feature subset selection→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Active Learning Literature Survey→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Semi-Supervised Learning→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Popular Ensemble Methods: An Empirical Study→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Human-level concept learning through probabilistic program induction→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Hierarchical Mixtures of Experts and the EM Algorithm→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Relational inductive biases, deep learning, and graph networks→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Ensemble deep learning: A review→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Ensembling neural networks: Many could be better than all→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Practical Recommendations for Gradient-Based Training of Deep Architectures→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Neural Network Ensembles, Cross Validation, and Active Learning→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Classification and Regression Trees.Legacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Multivariate Adaptive Regression SplinesLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Neural network ensemblesLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→The Analysis of Binary Data.Legacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Principles of Neurodynamics: Perceptrons and the Theory of Brain MechanismsLegacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Neural networks and principal component analysis: Learning from examples without local mi…Legacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Auto-association by multilayer perceptrons and singular value decompositionLegacy edge — source detail unavailable
• Semi-Supervised Learning→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
• Practical Recommendations for Gradient-Based Training of Deep Architectures→Learning representations by back-propagating errorsLegacy edge — source detail unavailable
• On the approximate realization of continuous mappings by neural networks→An introduction to computing with neural netsLegacy edge — source detail unavailable
• On the approximate realization of continuous mappings by neural networks→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Auto-association by multilayer perceptrons and singular value decomposition→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Auto-association by multilayer perceptrons and singular value decomposition→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Wrappers for feature subset selection→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
• Wrappers for feature subset selection→Classification and Regression Trees.Legacy edge — source detail unavailable
• Hierarchical Mixtures of Experts and the EM Algorithm→The Analysis of Binary Data.Legacy edge — source detail unavailable
• Hierarchical Mixtures of Experts and the EM Algorithm→Auto-association by multilayer perceptrons and singular value decompositionLegacy edge — source detail unavailable
• Hierarchical Mixtures of Experts and the EM Algorithm→Multivariate Adaptive Regression SplinesLegacy edge — source detail unavailable
• Hierarchical Mixtures of Experts and the EM Algorithm→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Hierarchical Mixtures of Experts and the EM Algorithm→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Extremely randomized trees→Multivariate Adaptive Regression SplinesLegacy edge — source detail unavailable
• Extremely randomized trees→An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…Legacy edge — source detail unavailable
• Extremely randomized trees→Classification and Regression Trees.Legacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Neural networks and principal component analysis: Learning from examples without local mi…Legacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Mitosis Detection in Breast Cancer Histology Images with Deep Neural NetworksLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural networks and principal component analysis: Learning from examples without local mi…→Auto-association by multilayer perceptrons and singular value decompositionLegacy edge — source detail unavailable
• Neural networks and principal component analysis: Learning from examples without local mi…→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Neural networks and principal component analysis: Learning from examples without local mi…→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• A training algorithm for optimal margin classifiers→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
• Ensembling neural networks: Many could be better than all→Hierarchical Mixtures of Experts and the EM AlgorithmLegacy edge — source detail unavailable
• Ensembling neural networks: Many could be better than all→Popular Ensemble Methods: An Empirical StudyLegacy edge — source detail unavailable
• Ensembling neural networks: Many could be better than all→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
• Ensembling neural networks: Many could be better than all→Neural network ensemblesLegacy edge — source detail unavailable
• Ensembling neural networks: Many could be better than all→An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…Legacy edge — source detail unavailable
• Ensembling neural networks: Many could be better than all→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Popular Ensemble Methods: An Empirical Study→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
• Popular Ensemble Methods: An Empirical Study→Neural network ensemblesLegacy edge — source detail unavailable
• Popular Ensemble Methods: An Empirical Study→An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Varia…Legacy edge — source detail unavailable
• Popular Ensemble Methods: An Empirical Study→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Multivariate Adaptive Regression Splines→Classification and Regression Trees.Legacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→On the approximate realization of continuous mappings by neural networksLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Approximation by superpositions of a sigmoidal function→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→A Tutorial on Support Vector Machines for Pattern RecognitionLegacy edge — source detail unavailable
• Neural Network Ensembles, Cross Validation, and Active Learning→Neural network ensemblesLegacy 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→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Neural network ensemblesLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural networks for classification: a survey→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Neural networks for classification: a survey→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Neural network ensembles→An introduction to computing with neural netsLegacy edge — source detail unavailable
• Neural network ensembles→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Multilayer feedforward networks are universal approximators→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Multilayer feedforward networks are universal approximators→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• A Tutorial on Support Vector Machines for Pattern Recognition→A training algorithm for optimal margin classifiersLegacy edge — source detail unavailable
• Relational inductive biases, deep learning, and graph networks→Principles of Neurodynamics: Perceptrons and the Theory of Brain MechanismsLegacy edge — source detail unavailable
• Relational inductive biases, deep learning, and graph networks→Deep learning in neural networks: An overviewLegacy edge — source detail unavailable
• Relational inductive biases, deep learning, and graph networks→Human-level concept learning through probabilistic program inductionLegacy edge — source detail unavailable
• Ensemble deep learning: A review→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
• Ensemble deep learning: A review→Neural network ensemblesLegacy edge — source detail unavailable