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

The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the...

machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning...

outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer...

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

Journal of Machine Learning Research. 2: 51–86. Hofmann, Thomas; Schölkopf, Bernhard; Smola, Alexander J. (2008). "Kernel methods in machine learning"...

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

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...

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

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, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters...

In machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). It can also improve the stability...

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

Nature Machine Intelligence is a monthly peer-reviewed scientific journal published by Nature Portfolio covering machine learning and artificial intelligence...

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

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

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves...

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...

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

Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions...

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 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, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation...

Embedding in machine learning refers to a representation learning technique that maps complex, high-dimensional data into a lower-dimensional vector space...

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

Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example...

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...

Human Machine Learning (RHML) is an interdisciplinary approach to designing human-AI interaction systems. RHML aims to enable continual learning between...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...