Pages that link to "Item:Q1370863"

The following pages link to A decision-theoretic generalization of on-line learning and an application to boosting (Q1370863):

Displaying 50 items.

• Variable selection and updating in model-based discriminant analysis for high dimensional data with food authenticity applications (Q977645) (← links)
• Learning with continuous experts using drifting games (Q982637) (← links)
• Boosting GARCH and neural networks for the prediction of heteroskedastic time series (Q984159) (← links)
• SVM-FuzCoC: A novel SVM-based feature selection method using a fuzzy complementary criterion (Q991247) (← links)
• Learn\(^{++}\).MF: A random subspace approach for the missing feature problem (Q991269) (← links)
• Information theoretic combination of pattern classifiers (Q991948) (← links)
• Generalized re-weighting local sampling mean discriminant analysis (Q991950) (← links)
• Cost-sensitive boosting for classification of imbalanced data (Q996413) (← links)
• New multicategory boosting algorithms based on multicategory Fisher-consistent losses (Q999662) (← links)
• Prediction of Alzheimer's diagnosis using semi-supervised distance metric learning with label propagation (Q1004962) (← links)
• View independent face detection based on horizontal rectangular features and accuracy improvement using combination kernel of various sizes (Q1005651) (← links)
• Exact bootstrap \(k\)-nearest neighbor learners (Q1009331) (← links)
• Surrogate maximization/minimization algorithms and extensions (Q1009342) (← links)
• Modeling churn using customer lifetime value (Q1011323) (← links)
• Parallelizing AdaBoost by weights dynamics (Q1019879) (← links)
• Classification by ensembles from random partitions of high-dimensional data (Q1020719) (← links)
• A stochastic approximation view of boosting (Q1020818) (← links)
• Efficient exploration of unknown indoor environments using a team of mobile robots (Q1022458) (← links)
• A local boosting algorithm for solving classification problems (Q1023522) (← links)
• Negative correlation in incremental learning (Q1024030) (← links)
• A \(\mathbb R\)eal generalization of discrete AdaBoost (Q1028894) (← links)
• Risk-sensitive loss functions for sparse multi-category classification problems (Q1031681) (← links)
• On generalization performance and non-convex optimization of extended \(\nu \)-support vector machine (Q1031941) (← links)
• Some challenges for statistics (Q1039967) (← links)
• A reference model for customer-centric data mining with support vector machines (Q1042173) (← links)
• The composite absolute penalties family for grouped and hierarchical variable selection (Q1043749) (← links)
• An efficient membership-query algorithm for learning DNF with respect to the uniform distribution (Q1384530) (← links)
• Potential-based algorithms in on-line prediction and game theory (Q1394787) (← links)
• Growing support vector classifiers with controlled complexity. (Q1403745) (← links)
• Vote counting measures for ensemble classifiers. (Q1425964) (← links)
• Constructing support vector machine ensemble. (Q1425966) (← links)
• On learning multicategory classification with sample queries. (Q1427857) (← links)
• A conversation with Leo Breiman. (Q1431203) (← links)
• A concrete statistical realization of Kleinberg's stochastic dicrimination for pattern recognition. I: Two-class classification (Q1431432) (← links)
• A geometric approach to leveraging weak learners (Q1603593) (← links)
• Drifting games and Brownian motion (Q1604221) (← links)
• Random average shifted histograms (Q1623662) (← links)
• Accurate ensemble pruning with PL-bagging (Q1623764) (← links)
• Stable feature selection for biomarker discovery (Q1631176) (← links)
• Noise peeling methods to improve boosting algorithms (Q1660240) (← links)
• On minimaxity of follow the leader strategy in the stochastic setting (Q1663642) (← links)
• Online multikernel learning based on a triple-norm regularizer for semantic image classification (Q1665404) (← links)
• Automatic emergence detection in complex systems (Q1674830) (← links)
• Probability estimation for multi-class classification using adaboost (Q1677008) (← links)
• Learning rotations with little regret (Q1689555) (← links)
• Context-based unsupervised ensemble learning and feature ranking (Q1689607) (← links)
• Analysis of web visit histories. II: Predicting navigation by nested STUMP regression trees (Q1695100) (← links)
• Robust regression using biased objectives (Q1698865) (← links)
• Sex with no regrets: how sexual reproduction uses a no regret learning algorithm for evolutionary advantage (Q1702267) (← links)
• Scale-free online learning (Q1704560) (← links)