concept

supervised learning

Also known as: supervised learning systems

Facts (9)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 3 facts

claimSupervised machine learning generates meaning from labeled training data, where each example consists of an input object (typically a vector quantity) and an enforced output value known as a supervisory signal (Praveena and Jaiganesh 2017).

claimThe three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning (Murphy 2012).

claimMurphy (2012) classifies the main types of machine learning as supervised learning, unsupervised learning, and reinforcement learning.

Track: Poster Session 3 - aistats 2026 virtual.aistats.org Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche · AISTATS 1 fact

claimExisting active feature acquisition (AFA) methods developed for supervised learning fail to address CATE challenges such as confounding, overlap, and structural similarities of potential outcomes.

A Comprehensive Benchmark and Evaluation Framework for Multi ... arxiv.org arXiv Jan 6, 2026 1 fact

referenceEeyore is a system for realistic depression simulation that utilizes expert-in-the-loop supervised and preference optimization, presented by Siyang Liu et al. in the Findings of the Association for Computational Linguistics: ACL 2025.

LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org arXiv Oct 23, 2025 1 fact

claimTraditional Knowledge Graph construction paradigms face three enduring challenges: scalability and data sparsity due to the failure of rule-based and supervised systems to generalize across domains; expert dependency and rigidity because schema and ontology design require substantial human intervention and lack adaptability; and pipeline fragmentation where disjoint handling of construction stages causes cumulative error propagation.

Quantum Approaches to Consciousness plato.stanford.edu Stanford Encyclopedia of Philosophy Nov 30, 2004 1 fact

claimAtmanspacher and Filk (2006) demonstrated that in simple supervised learning tasks, small recurrent networks learn both the prescribed input-output relation and the sequence in which inputs are presented, meaning recognition is impaired if the presentation sequence changes.

Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org medRxiv Nov 2, 2025 1 fact

claimAdaptations for supervised learning systems do not adequately address the unique challenges posed by generative AI.

Building Trustworthy NeuroSymbolic AI Systems - arXiv arxiv.org arXiv 1 fact

claimThe datasets DiSafety (Meade et al. 2023) and SafeTexT (Levy et al. 2022) are designed to induce safety in Language Models and Large Language Models through supervised learning.