In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. 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, feature learning or representation learning is a set of techniques that allows 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...

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

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...

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...

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...

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

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)...

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

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

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 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...

The machine learning-based attention method simulates how human attention works by freely assigning appropriate levels of importance to different components...

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...

Geometric feature learning is a technique combining machine learning and computer vision to solve visual tasks. The main goal of this method is to find...

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...

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

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...

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

In machine learning, a learning curve (or training curve) plots the optimal value of a model's loss function for a training set against this loss function...

step. Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization...

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

Multimodal learning, in the context of machine learning, is a type of deep learning using multiple modalities of data, such as text, audio, or images....

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

Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings in which multiple entities (often referred...

A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention mechanism, proposed in a 2017 paper...