The following pages link to AdaBoost.MH (Q20526):
Displaying 50 items.
- The reliability of classification of terminal nodes in GUIDE decision tree to predict the nonalcoholic fatty liver disease (Q2013940) (← links)
- Rotation Forests for regression (Q2016344) (← links)
- Learning causal effect using machine learning with application to China's typhoon (Q2023742) (← links)
- Analyzing cognitive processes from complex neuro-physiologically based data: some lessons (Q2023873) (← links)
- Sharpness estimation of combinatorial generalization ability bounds for threshold decision rules (Q2034842) (← links)
- Isotonic boosting classification rules (Q2036157) (← links)
- Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter (Q2037488) (← links)
- Batch mode active learning framework and its application on valuing large variable annuity portfolios (Q2038226) (← links)
- Boosting high dimensional predictive regressions with time varying parameters (Q2043255) (← links)
- SVM-boosting based on Markov resampling: theory and algorithm (Q2057733) (← links)
- Optimal classification scores based on multivariate marker transformations (Q2068896) (← links)
- Interpretable machine learning: fundamental principles and 10 grand challenges (Q2074414) (← links)
- A likelihood-based boosting algorithm for factor analysis models with binary data (Q2076167) (← links)
- Order scoring, bandit learning and order cancellations (Q2115951) (← links)
- A review on instance ranking problems in statistical learning (Q2127240) (← links)
- Recovering the time-dependent volatility in jump-diffusion models from nonlocal price observations (Q2128477) (← links)
- A comparative study of machine learning models for predicting the state of reactive mixing (Q2128488) (← links)
- Mathematical foundations of machine learning. Abstracts from the workshop held March 21--27, 2021 (hybrid meeting) (Q2131208) (← links)
- Inference in Bayesian additive vector autoregressive tree models (Q2135338) (← links)
- A precise high-dimensional asymptotic theory for boosting and minimum-\(\ell_1\)-norm interpolated classifiers (Q2148995) (← links)
- Polymorphic uncertainty quantification for engineering structures via a hyperplane modelling technique (Q2160447) (← links)
- A viral protein identifying framework based on temporal convolutional network (Q2160684) (← links)
- Handling concept drift via model reuse (Q2183593) (← links)
- Multi-label optimal margin distribution machine (Q2183598) (← links)
- Classification optimization for training a large dataset with naïve Bayes (Q2185824) (← links)
- Data science applications to string theory (Q2187812) (← links)
- Goal scoring, coherent loss and applications to machine learning (Q2191765) (← links)
- Quantitative convergence analysis of kernel based large-margin unified machines (Q2191836) (← links)
- Using LogitBoost classifier to predict protein structural classes (Q2194896) (← links)
- A robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noise (Q2201323) (← links)
- Propositionalization and embeddings: two sides of the same coin (Q2203327) (← links)
- Dynamic recursive tree-based partitioning for malignant melanoma identification in skin lesion dermoscopic images (Q2208386) (← links)
- A model-free Bayesian classifier (Q2212071) (← links)
- Fast construction of correcting ensembles for legacy artificial intelligence systems: algorithms and a case study (Q2213117) (← links)
- Fast greedy \(\mathcal{C} \)-bound minimization with guarantees (Q2217455) (← links)
- On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning (Q2218090) (← links)
- A review on distance based time series classification (Q2218332) (← links)
- Grafting for combinatorial binary model using frequent itemset mining (Q2218401) (← links)
- C443: a methodology to see a forest for the trees (Q2220704) (← links)
- Enhancing techniques for learning decision trees from imbalanced data (Q2228292) (← links)
- Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning (Q2238585) (← links)
- Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers (Q2251439) (← links)
- BoostingTree: parallel selection of weak learners in boosting, with application to ranking (Q2251442) (← links)
- Regret bounded by gradual variation for online convex optimization (Q2251474) (← links)
- Ensemble Gaussian mixture models for probability density estimation (Q2255769) (← links)
- A combination selection algorithm on forecasting (Q2256180) (← links)
- GA-Ensemble: a genetic algorithm for robust ensembles (Q2259225) (← links)
- A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics (Q2259821) (← links)
- Coronal loop detection from solar images (Q2270712) (← links)
- Supervised projection approach for boosting classifiers (Q2270792) (← links)
