Léon Bottou's own 2010 paper classifies the Widrow–Hoff LMS/Adaline update rule as stochastic gradient descent, matching the algorithm in the original 1960 paper

widrowhofflmsadalinesgdbottoustochastic-gradient-descenthistory-of-mlsourcing-floorbackpropagation-origins

Léon Bottou's working paper "Large-Scale Machine Learning with Stochastic Gradient Descent" — hosted on his own site and presented at COMPSTAT'2010 — contains a table titled "Stochastic gradient algorithms for various learning systems" whose first row is headed "Adaline (Widrow and Hoff, 1960)," giving the per-sample weight-update rule alongside entries for the Perceptron (Rosenblatt, 1957), k-Means, SVM, and Lasso. Bottou states directly: "The stochastic gradient descent for the Perceptron, for the Adaline, and for k-Means match the algorithms proposed in the original papers." This is a named, credentialed researcher's own technical analysis, on his own venue — not a paraphrase or aggregator summary — explicitly placing the Widrow–Hoff LMS/Adaline rule inside the stochastic-gradient-descent family.

This directly closes the sourcing gap question-verify-lms-is-stochastic-gradient-descent-primary identified: the vault's "LMS and Amari's rule are SGD siblings" claim had rested its LMS leg on a Tier-4 Wikipedia sentence alone. It also extends the same family tree back to its statistical root, claim-robbins-monro-1951-stochastic-approximation, and forward to the same lineage's other 1960s branch, Lucky's 1965 adaptive equalizer. See moc-backpropagation-origins.