Pages that link to "Item:Q127532"
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The following pages link to Greedy function approximation: A gradient boosting machine. (Q127532):
Displaying 50 items.
- Poisson dependency networks: gradient boosted models for multivariate count data (Q747270) (← links)
- Locally linear ensemble for regression (Q781904) (← links)
- Cultural consensus theory for the evaluation of patients' mental health scores in forensic psychiatric hospitals (Q826868) (← links)
- Mathematical optimization in classification and regression trees (Q828748) (← links)
- Deep distribution regression (Q830104) (← links)
- Angle-based cost-sensitive multicategory classification (Q830425) (← links)
- Embedding and learning with signatures (Q830467) (← links)
- The Delaunay triangulation learner and its ensembles (Q830534) (← links)
- Cost-sensitive ensemble learning: a unifying framework (Q832635) (← links)
- Representation in the (artificial) immune system (Q839513) (← links)
- An empirical study on classification methods for alarms from a bug-finding static C analyzer (Q845982) (← links)
- Improved customer choice predictions using ensemble methods (Q872292) (← links)
- Boosting kernel-based dimension reduction for jointly propagating spatial variability and parameter uncertainty in long-running flow simulators (Q887633) (← links)
- Variable selection for generalized linear mixed models by \(L_1\)-penalized estimation (Q892458) (← links)
- Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases (Q894696) (← links)
- Approximation of centroid end-points and switch points for replacing type reduction algorithms (Q900265) (← links)
- Ensemble classification of paired data (Q901582) (← links)
- A review of boosting methods for imbalanced data classification (Q903116) (← links)
- Boosting local quasi-likelihood estimators (Q904088) (← links)
- Bootstrap model selection for possibly dependent and heterogeneous data (Q904102) (← links)
- Marginal integration for nonparametric causal inference (Q908271) (← links)
- Inverse boosting for monotone regression functions (Q957242) (← links)
- Boosting and instability for regression trees (Q959181) (← links)
- Boosting additive models using component-wise P-splines (Q961113) (← links)
- Standard errors for bagged and random forest estimators (Q961193) (← links)
- Using boosting to prune double-bagging ensembles (Q961263) (← links)
- The Bayesian additive classification tree applied to credit risk modelling (Q962375) (← links)
- MARS: selecting basis functions and knots with an empirical Bayes method (Q964645) (← links)
- Navigating random forests and related advances in algorithmic modeling (Q975577) (← links)
- Boosting GARCH and neural networks for the prediction of heteroskedastic time series (Q984159) (← links)
- Functional dissipation microarrays for classification (Q996417) (← links)
- New multicategory boosting algorithms based on multicategory Fisher-consistent losses (Q999662) (← links)
- A dynamic model of expected bond returns: A functional gradient descent approach (Q1010570) (← links)
- Knot selection by boosting techniques (Q1020124) (← links)
- Boosting ridge regression (Q1020707) (← links)
- A stochastic approximation view of boosting (Q1020818) (← links)
- Robust learning from bites for data mining (Q1020821) (← links)
- A local boosting algorithm for solving classification problems (Q1023522) (← links)
- Logitboost with errors-in-variables (Q1023585) (← links)
- Robustified \(L_2\) boosting (Q1023674) (← links)
- On boosting kernel regression (Q1031760) (← links)
- Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients (Q1615281) (← links)
- A convex version of multivariate adaptive regression splines (Q1623730) (← links)
- Boosting techniques for nonlinear time series models (Q1633230) (← links)
- Using social media for classifying actionable insights in disaster scenario (Q1637506) (← links)
- NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC (Q1642583) (← links)
- Logitboost autoregressive networks (Q1654262) (← links)
- Gradient boosting for high-dimensional prediction of rare events (Q1658126) (← links)
- Noise peeling methods to improve boosting algorithms (Q1660240) (← links)
- A spatial-temporal-semantic neural network algorithm for location prediction on moving objects (Q1662700) (← links)
