The following pages link to AdaBoost.MH (Q20526):
Displaying 50 items.
- 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)
- Hierarchical design of fast minimum disagreement algorithms (Q1704562) (← links)
- LPiTrack: eye movement pattern recognition algorithm and application to biometric identification (Q1707478) (← links)
- Adaptive linear and normalized combination of radial basis function networks for function approximation and regression (Q1719381) (← links)
- Algorithms for drug sensitivity prediction (Q1736854) (← links)
- Learning customized and optimized lists of rules with mathematical programming (Q1741119) (← links)
- Instance-level accuracy versus bag-level accuracy in multi-instance learning (Q1741150) (← links)
- Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems (Q1741490) (← links)
- Membership-margin based feature selection for mixed type and high-dimensional data: theory and applications (Q1750036) (← links)
- Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S\&P 500 (Q1751873) (← links)
- Boosting-based sequential output prediction (Q1758664) (← links)
- Hierarchical classifiers for robust topological robot localization (Q1761266) (← links)
- Nonlinear models for ground-level ozone forecasting (Q1766975) (← links)
- Not so naive Bayes: Aggregating one-dependence estimators (Q1777411) (← links)
- Nonparametric bootstrap prediction (Q1781189) (← links)
- Deformation of log-likelihood loss function for multiclass boosting (Q1784701) (← links)
- A novel margin-based measure for directed hill climbing ensemble pruning (Q1793078) (← links)
- Arcing classifiers. (With discussion) (Q1807115) (← links)
- Boosting the margin: a new explanation for the effectiveness of voting methods (Q1807156) (← links)
- Regret in the on-line decision problem (Q1818283) (← links)
- Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors) (Q1848780) (← links)
- On weak base hypotheses and their implications for boosting regression and classification (Q1848929) (← links)
- An iterative mixed integer programming method for classification accuracy maximizing discriminant analysis (Q1869895) (← links)
- Top-down decision tree learning as information based boosting (Q1870539) (← links)
- Bounding the generalization error of convex combinations of classifiers: Balancing the dimensionality and the margins. (Q1872344) (← links)
- Generalization error of combined classifiers. (Q1872713) (← links)
- Least angle regression. (With discussion) (Q1879940) (← links)
- Generalization bounds for averaged classifiers (Q1879971) (← links)
- On approximating weighted sums with exponentially many terms (Q1880781) (← links)
- Process consistency for AdaBoost. (Q1884601) (← links)
- On the Bayes-risk consistency of regularized boosting methods. (Q1884602) (← links)
- Statistical behavior and consistency of classification methods based on convex risk minimization. (Q1884603) (← links)
- On domain-partitioning induction criteria: worst-case bounds for the worst-case based (Q1885908) (← links)
- Online learning in online auctions (Q1887078) (← links)
- Exploiting unlabeled data to enhance ensemble diversity (Q1944971) (← links)
- Online multiple kernel classification (Q1945032) (← links)
- Relational networks of conditional preferences (Q1945137) (← links)
- Random classification noise defeats all convex potential boosters (Q1959553) (← links)
- Extracting certainty from uncertainty: regret bounded by variation in costs (Q1959595) (← links)
- gBoost: a mathematical programming approach to graph classification and regression (Q1959643) (← links)
- Improved boosting algorithms using confidence-rated predictions (Q1969321) (← links)
- Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
- Multiclass classification, information, divergence and surrogate risk (Q1990579) (← links)
- Assessing robustness of classification using an angular breakdown point (Q1990584) (← links)
- A comparative study of the leading machine learning techniques and two new optimization algorithms (Q1991232) (← links)
- Multi-vehicle detection algorithm through combining Harr and HOG features (Q1997291) (← links)
- Calibrating AdaBoost for phoneme classification (Q2001122) (← links)
- Surrogate losses in passive and active learning (Q2008623) (← links)
