Historical milestone · 1960
ADALINE and the Widrow-Hoff rule
Reviewed through September 18, 2026
1960 · Historical milestone
ADALINE and the Widrow-Hoff rule
- Era
- 1960s
- Theme
- Machine-learning foundations
- Evidence form
- Adaptive switching circuits trained with a least-mean-squares error rule
- School / paradigm
- Connectionism / adaptive filtering
- Institution / context
- Stanford University
- Researchers
- Bernard Widrow; Marcian Hoff
School of thought
Connectionism
Matched on representative researcher.
Cognition emerges from learned distributed representations and weighted interactions among simple units.
Critique. Opacity, data/compute demands, unstable optimization, and weak guarantees or causal grounding.
Modern descendants. Foundation models, multimodal networks, representation learning, and differentiable agents.
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Showed that continuous error minimization could train a neuron-like linear unit and adapt online.
Result / historical claim. A single linear unit cannot solve nonlinearly separable tasks or learn hierarchical features.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: .
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Adaptive switching circuits trained with a least-mean-squares error rule
Limitation / debate. Stochastic gradient methods, online learning, and adaptive optimization.
Source status. This milestone row does not carry a primary-source URL in the approved export, and we do not have a verified link for it in our own research. We do not guess one.
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Reproduce
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Cite or share
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