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...

Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals...

Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent...

explicit algorithms. Feature learning can be either supervised, unsupervised, or self-supervised: In supervised feature learning, features are learned using...

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

Machine learning is commonly separated into three main learning paradigms, supervised learning, unsupervised learning and reinforcement learning. Each corresponds...

perform a specific task. Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labelled...

requiring learning rate warmup. Transformers typically are first pretrained by self-supervised learning on a large generic dataset, followed by supervised fine-tuning...

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

radial basis networks, another class of supervised neural network models). In recent developments of deep learning the rectified linear unit (ReLU) is more...

feedback, learning a reward model, and optimizing the policy. Compared to data collection for techniques like unsupervised or self-supervised learning, collecting...

Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

computation) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems...

design and analysis. AIARE encompasses several AI methodologies: Supervised learning employs tagged data to train models to recognize system components...

(GP) was a long-established technique in machine learning. GP is a form of semi-supervised learning where a model is first trained on a large, unlabeled...

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether...

Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured...

scenario, learning algorithms can actively query the user/teacher for labels. This type of iterative supervised learning is called active learning. Since...

Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations....

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...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

models primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets...

gather than input examples, semi-supervised learning can be useful. Regularizers have been designed to guide learning algorithms to learn models that respect...

Pllana, Sabri (2015). "The Potential of the Intel (R) Xeon Phi for Supervised Deep Learning". 2015 IEEE 17th International Conference on High Performance Computing...

Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by biological neural...

that TL would become the next driver of machine learning commercial success after supervised learning. In the 2020 paper, "Rethinking Pre-Training and...

predictability in the incoming data sequence, the highest level RNN can use supervised learning to easily classify even deep sequences with long intervals between...

are gene regulatory, protein interaction and metabolic networks. Supervised learning is a type of algorithm that learns from labeled data and learns how...

language models with many parameters, and are trained with self-supervised learning on a vast amount of text. This page lists notable large language...