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...

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

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...

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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...

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...

reducing bias. Boosting is a popular and effective technique used in supervised learning for both classification and regression tasks. The theoretical foundation...

unsupervised biologically inspired learning methods are available such as Hebbian learning and STDP, no effective supervised training method is suitable for...