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.

• Online learning from local features for video-based face recognition (Q632594) (← links)
• Variable selection for nonparametric Gaussian process priors: Models and computational strategies (Q635421) (← links)
• Survival ensembles by the sum of pairwise differences with application to lung cancer microarray studies (Q641157) (← links)
• A boosting method for maximization of the area under the ROC curve (Q645529) (← links)
• Representing and recognizing objects with massive local image patches (Q645863) (← links)
• A boosting approach for supervised Mahalanobis distance metric learning (Q645912) (← links)
• Sample-weighted clustering methods (Q660861) (← links)
• Unsupervised weight parameter estimation method for ensemble learning (Q662147) (← links)
• Online aggregation of unbounded losses using shifting experts with confidence (Q669288) (← links)
• From cluster ensemble to structure ensemble (Q712663) (← links)
• Tree models for difference and change detection in a complex environment (Q714372) (← links)
• Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers (Q733202) (← links)
• Projective morphoogies and their application in structural analysis of digital images (Q734150) (← links)
• Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (Q738768) (← links)
• Optimal learning for sequential sampling with non-parametric beliefs (Q742143) (← links)
• Component-wisely sparse boosting (Q743775) (← links)
• Machine learning feature selection methods for landslide susceptibility mapping (Q745733) (← links)
• Fast pedestrian detection system with a two layer cascade of classifiers (Q745831) (← links)
• Soft-max boosting (Q747255) (← links)
• Hybrid cluster ensemble framework based on the random combination of data transformation operators (Q763364) (← links)
• Variable selection using penalized empirical likelihood (Q763671) (← links)
• Mathematical optimization in classification and regression trees (Q828748) (← links)
• Angle-based cost-sensitive multicategory classification (Q830425) (← links)
• Cost-sensitive ensemble learning: a unifying framework (Q832635) (← links)
• Representation in the (artificial) immune system (Q839513) (← links)
• Identifying the interacting positions of a protein using Boolean learning and support vector machines (Q849527) (← links)
• Tutorial series on brain-inspired computing. VI: Geometrical structure of boosting algorithm (Q857990) (← links)
• Boosting of granular models (Q869128) (← links)
• Improved customer choice predictions using ensemble methods (Q872292) (← links)
• Breast cancer prediction using the isotonic separation technique (Q877069) (← links)
• ML-KNN: A lazy learning approach to multi-label learning (Q882226) (← links)
• Face detection with boosted Gaussian features (Q882272) (← links)
• Knowledge acquisition and development of accurate rules for predicting protein stability changes (Q884265) (← links)
• Self-improved gaps almost everywhere for the agnostic approximation of monomials (Q884469) (← links)
• A Fisher consistent multiclass loss function with variable margin on positive examples (Q887268) (← links)
• Performance improvement of classifier fusion for batch samples based on upper integral (Q889383) (← links)
• Probabilistic combination of classification rules and its application to medical diagnosis (Q890315) (← links)
• Nonstochastic bandits: Countable decision set, unbounded costs and reactive environments (Q924170) (← links)
• Boosting multi-features with prior knowledge for mini unmanned helicopter landmark detection (Q926121) (← links)
• The value of agreement a new boosting algorithm (Q927875) (← links)
• A lazy bagging approach to classification (Q936407) (← links)
• An extensive comparison of recent classification tools applied to microarray data (Q957169) (← links)
• Boosting and instability for regression trees (Q959181) (← links)
• Boosting additive models using component-wise P-splines (Q961113) (← links)
• Using boosting to prune double-bagging ensembles (Q961263) (← links)
• Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap (Q961845) (← links)
• The Bayesian additive classification tree applied to credit risk modelling (Q962375) (← links)
• A study on iris localization and recognition on mobile phones (Q966684) (← links)
• Heterogeneous stacking for classification-driven watershed segmentation (Q966772) (← links)
• Least angle and \(\ell _{1}\) penalized regression: a review (Q975564) (← links)