Statistical Learning Theory

Citation edge evidence

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

• A Survey on Transfer Learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• A Tutorial on Support Vector Machines for Pattern Recognition→Statistical Learning TheoryLegacy edge — source detail unavailable
• Gaussian Processes for Machine Learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• The Graph Neural Network Model→Statistical Learning TheoryLegacy edge — source detail unavailable
• Least Squares Support Vector Machine Classifiers→Statistical Learning TheoryLegacy edge — source detail unavailable
• An overview of statistical learning theory→Statistical Learning TheoryLegacy edge — source detail unavailable
• Survey of Clustering Algorithms→Statistical Learning TheoryLegacy edge — source detail unavailable
• Estimating the Support of a High-Dimensional Distribution→Statistical Learning TheoryLegacy edge — source detail unavailable
• mixup: Beyond Empirical Risk Minimization→Statistical Learning TheoryLegacy edge — source detail unavailable
• Semi-Supervised Learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based o…→Statistical Learning TheoryLegacy edge — source detail unavailable
• Semi-Supervised Learning Literature Survey→Statistical Learning TheoryLegacy edge — source detail unavailable
• Learning with Local and Global Consistency→Statistical Learning TheoryLegacy edge — source detail unavailable
• A theory of learning from different domains→Statistical Learning TheoryLegacy edge — source detail unavailable
• Support Vector Data Description→Statistical Learning TheoryLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→Statistical Learning TheoryLegacy edge — source detail unavailable
• Prediction, Learning, and Games→Statistical Learning TheoryLegacy edge — source detail unavailable
• Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…→Statistical Learning TheoryLegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→Statistical Learning TheoryLegacy edge — source detail unavailable
• Apprenticeship learning via inverse reinforcement learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• Support Vector Machines for Classification and Regression→Statistical Learning TheoryLegacy edge — source detail unavailable
• Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→Statistical Learning TheoryLegacy edge — source detail unavailable
• A survey on semi-supervised learning→Statistical Learning TheoryLegacy 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
• Understanding deep learning (still) requires rethinking generalization→Statistical Learning TheoryLegacy edge — source detail unavailable
• Choosing Multiple Parameters for Support Vector Machines→Statistical Learning TheoryLegacy edge — source detail unavailable
• Using AUC and accuracy in evaluating learning algorithms→Statistical Learning TheoryLegacy edge — source detail unavailable
• Support Vector Method for Novelty Detection→Statistical Learning TheoryLegacy edge — source detail unavailable
• Analysis of Representations for Domain Adaptation→Statistical Learning TheoryLegacy edge — source detail unavailable
• Large Margin Methods for Structured and Interdependent Output Variables→Statistical Learning TheoryLegacy edge — source detail unavailable
• Extreme learning machines: a survey→Statistical Learning TheoryLegacy edge — source detail unavailable
• On Discriminative vs. Generative Classifiers: A comparison of logistic regression and nai…→Statistical Learning TheoryLegacy edge — source detail unavailable
• kernlab - An S4 Package for Kernel Methods in R→Statistical Learning TheoryLegacy edge — source detail unavailable
• Introduction to Semi-Supervised Learning→Statistical Learning TheoryLegacy edge — source detail unavailable
• On the algorithmic implementation of multiclass kernel-based vector machines→Statistical Learning TheoryLegacy edge — source detail unavailable
• Statistical Learning Theory→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Semi-Supervised Learning→Learning with Local and Global ConsistencyLegacy edge — source detail unavailable
• Semi-Supervised Learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Semi-Supervised Learning→On Discriminative vs. Generative Classifiers: A comparison of logistic regression and nai…Legacy 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
• Least Squares Support Vector Machine Classifiers→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
• Support Vector Machines for Classification and Regression→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Support Vector Data Description→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
• Support Vector Data Description→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Introduction to Semi-Supervised Learning→Semi-Supervised LearningLegacy edge — source detail unavailable
• Introduction to Semi-Supervised Learning→Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…Legacy edge — source detail unavailable
• Introduction to Semi-Supervised Learning→Large Margin Methods for Structured and Interdependent Output VariablesLegacy edge — source detail unavailable
• Introduction to Semi-Supervised Learning→Semi-Supervised Learning Literature SurveyLegacy edge — source detail unavailable
• Introduction to Semi-Supervised Learning→Learning with Local and Global ConsistencyLegacy edge — source detail unavailable
• Extreme learning machines: a survey→Least Squares Support Vector Machine ClassifiersLegacy edge — source detail unavailable
• Extreme learning machines: a survey→Universal Approximation using Incremental Constructive Feedforward Networks with Random H…Legacy edge — source detail unavailable
• Apprenticeship learning via inverse reinforcement learning→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Using AUC and accuracy in evaluating learning algorithms→A Tutorial on Support Vector Machines for Pattern RecognitionLegacy edge — source detail unavailable
• A theory of learning from different domains→Analysis of Representations for Domain AdaptationLegacy edge — source detail unavailable
• Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…→Semi-Supervised LearningLegacy edge — source detail unavailable
• Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…→Learning with Local and Global ConsistencyLegacy edge — source detail unavailable
• Support Vector Method for Novelty Detection→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
• Large Margin Methods for Structured and Interdependent Output Variables→On the algorithmic implementation of multiclass kernel-based vector machinesLegacy edge — source detail unavailable
• An introduction to kernel-based learning algorithms→Support Vector Method for Novelty DetectionLegacy 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→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• The Graph Neural Network Model→Semi-Supervised LearningLegacy edge — source detail unavailable
• The Graph Neural Network Model→Universal Approximation using Incremental Constructive Feedforward Networks with Random H…Legacy 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→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Semi-Supervised Learning Literature Survey→Semi-Supervised LearningLegacy edge — source detail unavailable
• Semi-Supervised Learning Literature Survey→Learning with Local and Global ConsistencyLegacy edge — source detail unavailable
• A Tutorial on Support Vector Machines for Pattern Recognition→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
• Survey of Clustering Algorithms→An introduction to kernel-based learning algorithmsLegacy edge — source detail unavailable
• Survey of Clustering Algorithms→A Tutorial on Support Vector Machines for Pattern RecognitionLegacy edge — source detail unavailable
• On the algorithmic implementation of multiclass kernel-based vector machines→A Tutorial on Support Vector Machines for Pattern RecognitionLegacy edge — source detail unavailable
• kernlab - An S4 Package for Kernel Methods in R→Estimating the Support of a High-Dimensional DistributionLegacy edge — source detail unavailable
• kernlab - An S4 Package for Kernel Methods in R→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
• A Survey on Transfer Learning→Analysis of Representations for Domain AdaptationLegacy edge — source detail unavailable
• A Survey on Transfer Learning→Semi-Supervised Learning Literature SurveyLegacy edge — source detail unavailable
• A survey on semi-supervised learning→Semi-Supervised LearningLegacy edge — source detail unavailable
• A survey on semi-supervised learning→Introduction to Semi-Supervised LearningLegacy edge — source detail unavailable
• A survey on semi-supervised learning→Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…Legacy edge — source detail unavailable
• A survey on semi-supervised learning→Semi-Supervised Learning Literature SurveyLegacy edge — source detail unavailable
• A survey on semi-supervised learning→Learning with Local and Global ConsistencyLegacy edge — source detail unavailable
• A survey on semi-supervised learning→mixup: Beyond Empirical Risk MinimizationLegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→Prediction, Learning, and GamesLegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→The Nature of Statistical Learning TheoryLegacy edge — source detail unavailable
• Understanding Machine Learning: From Theory To Algorithms→On the algorithmic implementation of multiclass kernel-based vector machinesLegacy edge — source detail unavailable