Widrow's 1966 'Bootstrap Learning' paper documents a genuine, preliminary, non-gradient attempt at training multilayer networks of adaptive threshold elements
Unlike Steinbuch & Widrow's 1965 note (claim-steinbuch-widrow-1965-comparison-not-multilayer-training), Widrow's 1966 IFAC paper is squarely about multilayer training. Its introduction states its subject includes "convergent adaptation procedures for multilayered and more generally-connected networks of adaptive threshold elements," and its closing "Current and Future Research" section reports: "Preliminary studies have been made with some success toward the development of adaptation algorithms for multilayered networks of adaptive threshold elements using the selective bootstrap principle."
The mechanism is a global, non-gradient reinforcement scheme, distinct from the Madaline Rule I architecture (adaptive first layer, fixed second layer) already documented in claim-madaline-rule-i-first-layer-trainable-second-fixed: "If performance observed at a set of output terminals is 'better than average,' every element in the net receives positive bootstrap adaptation. If output-terminal performance is poorer than average, then all elements receive negative bootstrap adaptation." Every adaptive element in the network moves the same direction based only on aggregate output quality — no per-element credit assignment. The paper frames this network-wide extension as future/ongoing work rather than a completed result; the bulk of the text analytically derives and experimentally verifies the single-element case (applied to simulated Blackjack play).
This is a second, earlier-dated, and independently primary-documented instance of the 1960s multilayer-training effort than Madaline Rule I, and it corroborates Widrow & Lehr's 1990 retrospective statement that the group's attempts to "develop learning rules for networks with multiple adaptive layers were unsuccessful" before the group moved to adaptive signal processing (claim-widrow-abandoned-multilayer-training-until-1985-backprop). See moc-backpropagation-origins.
Source
“If performance observed at a set of output terminals is "better than average," every element in the net receives positive bootstrap adaptation.”
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