Gradient descent technology for sparse vector learning in ontology algorithms

DOI10.1080/09720529.2016.1178934zbMath1487.68224OpenAlexW2466985653WikidataQ112808024 ScholiaQ112808024MaRDI QIDQ5069813

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Publication date: 19 April 2022

Published in: Journal of Discrete Mathematical Sciences and Cryptography (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/09720529.2016.1178934

zbMATH Keywords

similarity measuregradient descentontologysparse vectorontology mapping

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Knowledge representation (68T30)

Uses Software

• chemometrics

Cites Work

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• Stability analysis of learning algorithms for ontology similarity computation
• Performance of first-order methods for smooth convex minimization: a novel approach
• An inexact and nonmonotone proximal method for smooth unconstrained minimization
• A very simple SQCQP method for a class of smooth convex constrained minimization problems with nice convergence results
• Extreme value analysis of empirical frame coefficients and implications for denoising by soft-thresholding
• On a generalization of the iterative soft-thresholding algorithm for the case of non-separable penalty
• Thinned Array Beampattern Synthesis by Iterative Soft-Thresholding-Based Optimization Algorithms
• Cross‐validation based assessment of a new Bayesian palaeoclimate model

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