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

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

The machine learning-based attention method simulates how human attention works by assigning varying levels of importance to different words in a sentence...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function...

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, variance. It is used in supervised learning and a family of machine...

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)...

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...

terminology "learning internal spotlights of attention" was introduced in 1993. In 1993, the IBM alignment models were used for statistical machine translation...

outline is provided as an overview of and topical guide to machine learning: Machine learning – subfield of soft computing within computer science that...

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

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating...

develops and studies methods and software that enable machines to perceive their environment and uses learning and intelligence to take actions that maximize...

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

Fairness in machine learning refers to the various attempts at correcting algorithmic bias in automated decision processes based on machine learning models...

In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which...

page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. History of...

Machine Learning is a peer-reviewed scientific journal, published since 1986. In 2001, forty editors and members of the editorial board of Machine Learning...

Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value...

Tensor informally refers in machine learning to two different concepts that organize and represent data. Data may be organized in a multidimensional array...

In machine learning, a hyperparameter is a parameter, such as the learning rate or choice of optimizer, which specifies details of the learning process...

open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms...

Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of...

semi-supervised learning, since Vapnik's motivation is quite different. An example of an algorithm in this category is the Transductive Support Vector Machine (TSVM)...

Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems...

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...

AI (XAI), often overlapping with Interpretable AI, or Explainable Machine Learning (XML), either refers to an artificial intelligence (AI) system over...

learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with...

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

non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single...

Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory which solves optimal control problems with methods...