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...

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

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

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

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

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

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...

The transformer is a deep learning architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called...

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

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

(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"...

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

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

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)...

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

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

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

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

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...

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

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

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, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

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...

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 machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable...