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...

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

Distance education Online school Online learning in higher education Online tutoring Massive open online courses Online machine learning, in computer science...

incremental learning policies in the field of online machine learning. Using active learning allows for faster development of a machine learning algorithm...

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

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

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...

involves online education (also known as online learning, remote learning or remote education) through an online school. A distance learning program can...

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...

to self-archive their papers online after peer-review. J.R. Quinlan (1986). "Induction of Decision Trees". Machine Learning. 1: 81–106. doi:10.1007/BF00116251...

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

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

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

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

encompasses several domains including learning theory, computer-based training, online learning, and m-learning where mobile technologies are used. 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...

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...

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

The International Conference on Machine Learning (ICML) is a 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...

Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control...

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

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

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

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

become an important optimization method in machine learning. Both statistical estimation and machine learning consider the problem of minimizing an objective...

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

AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), is a field of research within artificial intelligence (AI) that...

and communication of the nodes Subsamples of large data sets and online machine learning There are many reasons for wanting to distribute intelligence or...