Neural networks for classification: a survey

Citation edge evidence

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• Supervised Machine Learning: A Review of Classification Techniques→Neural networks for classification: a surveyLegacy edge — source detail unavailable
• Machine learning: a review of classification and combining techniques→Neural networks for classification: a surveyLegacy edge — source detail unavailable
• A review of clustering techniques and developments→Neural networks for classification: a surveyLegacy 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→An Introduction to the BootstrapLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Neural networks for pattern recognitionLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Bagging PredictorsLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Bagging predictorsLegacy 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 Networks for Pattern RecognitionLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Introduction to Statistical Pattern RecognitionLegacy 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→Stacked generalizationLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Training feedforward networks with the Marquardt algorithmLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Probability, Random Variables, and Stochastic ProcessesLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Introduction to the theory of neural computationLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Approximation capabilities of multilayer feedforward networksLegacy edge — source detail unavailable
• Neural networks for classification: a survey→On combining classifiersLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Random variables and stochastic processesLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Neural network ensemblesLegacy 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→A Practical Bayesian Framework for Backpropagation NetworksLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Pattern recognition : a statistical approachLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Solving Multiclass Learning Problems via Error-Correcting Output CodesLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Using mutual information for selecting features in supervised neural net learningLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Optimal Brain DamageLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Machine Learning, Neural and Statistical ClassificationLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Wavelet networksLegacy 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→Pruning algorithms-a surveyLegacy 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→Optimal unsupervised learning in a single-layer linear feedforward neural networkLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Decision combination in multiple classifier systemsLegacy edge — source detail unavailable
• Neural networks for classification: a survey→Interpreting neural-network connection weightsLegacy edge — source detail unavailable
• Supervised Machine Learning: A Review of Classification Techniques→Bagging PredictorsLegacy edge — source detail unavailable
• Approximation capabilities of multilayer feedforward networks→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Approximation capabilities of multilayer feedforward networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Approximation capabilities of multilayer feedforward networks→Multilayer feedforward networks are universal approximatorsLegacy 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→Neural network ensemblesLegacy 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→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Neural Networks and the Bias/Variance Dilemma→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Machine learning: a review of classification and combining techniques→Bagging PredictorsLegacy 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
• A Practical Bayesian Framework for Backpropagation Networks→Optimal Brain DamageLegacy edge — source detail unavailable
• Optimal Brain Damage→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• Neural Network Ensembles, Cross Validation, and Active Learning→Neural Networks and the Bias/Variance DilemmaLegacy edge — source detail unavailable
• Neural Network Ensembles, Cross Validation, and Active Learning→Neural network ensemblesLegacy edge — source detail unavailable
• Neural Network Ensembles, Cross Validation, and Active Learning→Stacked generalizationLegacy 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
• Pruning algorithms-a survey→What Size Net Gives Valid Generalization?Legacy edge — source detail unavailable
• On combining classifiers→Pattern recognition : a statistical approachLegacy edge — source detail unavailable
• On combining classifiers→Decision combination in multiple classifier systemsLegacy edge — source detail unavailable
• On combining classifiers→Neural Network Ensembles, Cross Validation, and Active LearningLegacy edge — source detail unavailable
• On combining classifiers→Neural network ensemblesLegacy edge — source detail unavailable
• On combining classifiers→Stacked generalizationLegacy edge — source detail unavailable
• On combining classifiers→Bagging PredictorsLegacy edge — source detail unavailable
• On combining classifiers→Bagging predictorsLegacy edge — source detail unavailable
• Wavelet networks→Approximation by superpositions of a sigmoidal functionLegacy edge — source detail unavailable
• Wavelet networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Wavelet networks→Multilayer feedforward networks are universal approximatorsLegacy edge — source detail unavailable
• Bagging Predictors→Machine Learning, Neural and Statistical ClassificationLegacy edge — source detail unavailable
• Bagging Predictors→An Introduction to the BootstrapLegacy edge — source detail unavailable
• Bagging predictors→Machine Learning, Neural and Statistical ClassificationLegacy edge — source detail unavailable
• Bagging predictors→An Introduction to the BootstrapLegacy edge — source detail unavailable