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

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

In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations...

Feature engineering is a preprocessing step in supervised machine learning and statistical modeling which transforms raw data into a more effective set...

amenable for machine learning, an expert may have to apply appropriate data pre-processing, feature engineering, feature extraction, and feature selection...

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

In machine learning (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single...

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

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

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

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 (QML) is the study of quantum algorithms which solve machine learning tasks. The most common use of the term refers to quantum...

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

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

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

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, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based...

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

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

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

In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and...

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

corner or blob Feature (machine learning), in statistics: individual measurable properties of the phenomena being observed Software feature, a distinguishing...

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...

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

explainable AI (XAI), often overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans...