In machine learning, supervised learning (SL) is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired...

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...

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

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

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

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

models commonly employed supervised learning from large amounts of manually-labeled data. The reliance on supervised learning limited their use on datasets...

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

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

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

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

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

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

much more flexible structure to exist among those alternatives. Supervised learning algorithms search through a hypothesis space to find a suitable hypothesis...

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

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

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

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

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

In logic, statistical inference, and supervised learning, transduction or transductive inference is reasoning from observed, specific (training) cases...

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

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

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

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

Alexandru; Caruana, Rich (2005). Predicting good probabilities with supervised learning (PDF). ICML. doi:10.1145/1102351.1102430. Olivier Chapelle; Vladimir...

visual scenes even when the objects are shifted. Several supervised and unsupervised learning algorithms have been proposed over the decades to train the...

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...

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...