The Nature of Statistical Learning Theory

Citation edge evidence

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• Greedy function approximation: A gradient boosting machine.→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Statistical Learning Theory→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• A Tutorial on Support Vector Machines for Pattern Recognition→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Anomaly detection→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Learning from Imbalanced Data→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Least Squares Support Vector Machine Classifiers→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Nonlinear Component Analysis as a Kernel Eigenvalue Problem→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Text categorization with Support Vector Machines: Learning with many relevant features→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• The random subspace method for constructing decision forests→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Pattern Recognition and Neural Networks→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• An overview of statistical learning theory→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Estimating the Support of a High-Dimensional Distribution→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• A Practical Guide to Support Vector Classication→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Quantum machine learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Semi-Supervised Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Supervised Machine Learning: A Review of Classification Techniques→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Front-End Factor Analysis for Speaker Verification→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Support Vector Data Description→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• A few useful things to know about machine learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Sentiment analysis algorithms and applications: A survey→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• A Short Introduction to Boosting→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Apprenticeship learning via inverse reinforcement learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Support Vector Machines for Classification and Regression→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• A re-examination of text categorization methods→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Boosting the margin: a new explanation for the effectiveness of voting methods→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Logistic regression and artificial neural network classification models: a methodology re…→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Choosing Multiple Parameters for Support Vector Machines→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Semi-Supervised Learning→A re-examination of text categorization methodsLegacy edge — source detail unavailable
• Semi-Supervised Learning→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
• Semi-Supervised Learning→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
• Semi-Supervised Learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• Semi-Supervised Learning→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
• A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…→Statistical Learning TheoryLegacy edge — source detail unavailable
• Least Squares Support Vector Machine Classifiers→Statistical Learning TheoryLegacy edge — source detail unavailable
• Greedy function approximation: A gradient boosting machine.→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• Support Vector Machines for Classification and Regression→Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…Legacy edge — source detail unavailable
• Support Vector Machines for Classification and Regression→Statistical Learning TheoryLegacy edge — source detail unavailable
• Supervised Machine Learning: A Review of Classification Techniques→A Tutorial on Support Vector Machines for Pattern RecognitionLegacy edge — source detail unavailable
• Support Vector Data Description→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
• Support Vector Data Description→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
• Support Vector Data Description→Statistical Learning TheoryLegacy edge — source detail unavailable
• Logistic regression and artificial neural network classification models: a methodology re…→Pattern Recognition and Neural NetworksLegacy edge — source detail unavailable
• Apprenticeship learning via inverse reinforcement learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• A re-examination of text categorization methods→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• Learning Deep Architectures for AI→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
• Deep learning in neural networks: An overview→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)→The random subspace method for constructing decision forestsLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→Boosting the margin: a new explanation for the effectiveness of voting methodsLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→A Tutorial on Support Vector Machines for Pattern RecognitionLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→Statistical Learning TheoryLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→Text categorization with Support Vector Machines: Learning with many relevant featuresLegacy edge — source detail unavailable
• Learning from Imbalanced Data→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
• Anomaly detection→Support Vector Data DescriptionLegacy edge — source detail unavailable
• Anomaly detection→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
• Estimating the Support of a High-Dimensional Distribution→An introduction to kernel-based learning algorithmsLegacy edge — source detail unavailable
• Estimating the Support of a High-Dimensional Distribution→Statistical Learning TheoryLegacy edge — source detail unavailable
• A Tutorial on Support Vector Machines for Pattern Recognition→Support Vector Method for Function Approximation, Regression Estimation and Signal Proces…Legacy edge — source detail unavailable
• A Tutorial on Support Vector Machines for Pattern Recognition→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
• A Tutorial on Support Vector Machines for Pattern Recognition→Statistical Learning TheoryLegacy edge — source detail unavailable
• Nonlinear Component Analysis as a Kernel Eigenvalue Problem→An overview of statistical learning theoryLegacy 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
• An overview of statistical learning theory→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
• An overview of statistical learning theory→Statistical Learning TheoryLegacy edge — source detail unavailable
• Choosing Multiple Parameters for Support Vector Machines→Statistical Learning TheoryLegacy edge — source detail unavailable
• A few useful things to know about machine learning→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review→Deep learning in neural networks: An overviewLegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→Learning Deep Architectures for AILegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→A Practical Guide to Support Vector ClassicationLegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→Nonlinear Component Analysis as a Kernel Eigenvalue ProblemLegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→Statistical Learning TheoryLegacy edge — source detail unavailable