ramifications in machine learning and statistics, most notably leading to the development of boosting. Initially, 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 instead of residuals as...

"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...

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...

in machine learning, based on connected, hierarchical functionsPages displaying short descriptions of redirect targets Boosting (machine learning) – Method...

variables. Boosting – Method in machine learning Decision tree learning – Machine learning algorithm Ensemble learning – Statistics and machine learning technique...

XGBoost (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python...

Generalization error Regularization (mathematics) Statistical learning theory Boosting (machine learning) Cross-validation, in particular using a "validation set"...

The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was...

short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by...

(t-SNE) Ensemble learning AdaBoost Boosting Bootstrap aggregating (also "bagging" or "bootstrapping") Ensemble averaging Gradient boosted decision tree (GBDT)...

CatBoost, a gradient boosting machine learning library". TechCrunch. Yegulalp, Serdar (July 28, 2017). "Yandex open sources CatBoost machine learning library"...

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

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

Boosting (behavioral science), a technique to improve human decisions Boosting (machine learning), a supervised learning algorithm Intel Turbo Boost,...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

AdaBoost (short for Adaptive Boosting) is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

In machine learning, supervised learning (SL) is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired...

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...

perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy". Boosting (machine learning) Bootstrapping...

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination...

Attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that...

Theoretical results in machine learning mainly deal with a type of inductive learning called supervised learning. In supervised learning, an algorithm is given...

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

In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update...

In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani...

In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization...

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...