Pages that link to "Item:Q127532"

The following pages link to Greedy function approximation: A gradient boosting machine. (Q127532):

Displaying 50 items.

• An update on statistical boosting in biomedicine (Q1664502) (← links)
• A unified definition of mutual information with applications in machine learning (Q1664976) (← links)
• Random forest with adaptive local template for pedestrian detection (Q1666540) (← links)
• Nonlinear multi-output regression on unknown input manifold (Q1680851) (← links)
• Boosting flexible functional regression models with a high number of functional historical effects (Q1703807) (← links)
• Bayesian additive regression trees using Bayesian model averaging (Q1704023) (← links)
• Pathway-based kernel boosting for the analysis of genome-wide association studies (Q1705355) (← links)
• LPiTrack: eye movement pattern recognition algorithm and application to biometric identification (Q1707478) (← links)
• Regression with stagewise minimization on risk function (Q1727927) (← links)
• Tree ensembles with rule structured horseshoe regularization (Q1728658) (← links)
• Improving the prediction performance of the Lasso by subtracting the additive structural noises (Q1729359) (← links)
• Covariate balancing propensity score by tailored loss functions (Q1731067) (← links)
• Bootstrap -- an exploration (Q1731214) (← links)
• Credit spread approximation and improvement using random forest regression (Q1735198) (← links)
• Copula theory and probabilistic sensitivity analysis: is there a connection? (Q1740560) (← links)
• Student and school performance across countries: a machine learning approach (Q1749516) (← links)
• A nearest neighbour extension to project duration forecasting with artificial intelligence (Q1751933) (← links)
• Local greedy approximation for nonlinear regression and neural network training. (Q1848831) (← links)
• Least angle regression. (With discussion) (Q1879940) (← links)
• Learned-loss boosting (Q1927174) (← links)
• Forecasting with many predictors: is boosting a viable alternative? (Q1942870) (← links)
• Machine learning based classification of normal, slow and fast walking by extracting multimodal features from stride interval time series (Q1980091) (← links)
• Universal sieve-based strategies for efficient estimation using machine learning tools (Q1983607) (← links)
• KLERC: kernel Lagrangian expectile regression calculator (Q1995837) (← links)
• Analysis of a two-layer neural network via displacement convexity (Q1996787) (← links)
• Utilizing data mining techniques to predict expected freeway travel time from experienced travel time (Q1997293) (← links)
• A data-driven newsvendor problem: from data to decision (Q1999637) (← links)
• Evaluating the impact of a HIV low-risk express care task-shifting program: a case study of the targeted learning roadmap (Q2001893) (← links)
• Direct cellularity estimation on breast cancer histopathology images using transfer learning (Q2003647) (← links)
• Using machine learning algorithms to predict hepatitis B surface antigen seroclearance (Q2003677) (← links)
• Learning causal effect using machine learning with application to China's typhoon (Q2023742) (← links)
• Analytics for labor planning in systems with load-dependent service times (Q2023944) (← links)
• Retail sales forecasting with meta-learning (Q2028846) (← links)
• Regularizing axis-aligned ensembles via data rotations that favor simpler learners (Q2029102) (← links)
• Predicting mortgage early delinquency with machine learning methods (Q2029349) (← links)
• Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter (Q2037488) (← links)
• Stochastic approximation: from statistical origin to big-data, multidisciplinary applications (Q2038304) (← links)
• Boosting high dimensional predictive regressions with time varying parameters (Q2043255) (← links)
• Estimation of a density using an improved surrogate model (Q2044316) (← links)
• Consistent regression using data-dependent coverings (Q2044358) (← links)
• Adaptive covariate acquisition for minimizing total cost of classification (Q2051306) (← links)
• Boosted nonparametric hazards with time-dependent covariates (Q2054480) (← links)
• A deeper look at machine learning-based cryptanalysis (Q2056717) (← links)
• Bayesian additive regression trees with model trees (Q2058722) (← links)
• Temporal mixture ensemble models for probabilistic forecasting of intraday cryptocurrency volume (Q2064616) (← links)
• Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance (Q2066736) (← links)
• Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data (Q2066785) (← links)
• RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods (Q2071508) (← links)
• Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities (Q2072477) (← links)
• Classifying sleep states using persistent homology and Markov chains: a pilot study (Q2072596) (← links)