ramifications in machine learning and statistics, most notably leading to the development of boosting. When first introduced, the hypothesis boosting problem simply...

Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals rather than the typical...

"catboost/catboost". GitHub. "Yandex open sources CatBoost, a gradient boosting machine learning library". TechCrunch. 18 July 2017. Retrieved 2020-08-30...

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...

Machine Learning. 27: 1–14. Robert E. Schapire and Yoram Singer (1999). "Improved Boosting Algorithms Using Confidence-rated Predictions". Machine Learning...

out." Gradient boosted decision tree (GBDT) Gradient boosting machine (GBM) Random Forest Stacked Generalization (blending) Meta-learning Inductive bias...

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning...

International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning. Along with NeurIPS and ICLR...

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related...

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...