alleryStart learning →The LMS (Widrow–Hoff) update rule on a single training example is
Aθⱼ := θⱼ · y⁽ⁱ⁾
Bθⱼ := θⱼ + α · (y⁽ⁱ⁾ − h_θ(x⁽ⁱ⁾)) · xⱼ⁽ⁱ⁾
Cθⱼ := θⱼ − α · y⁽ⁱ⁾
Dθⱼ := xⱼ⁽ⁱ⁾ − y⁽ⁱ⁾
Answer & Solution
Correct answer: B. θⱼ := θⱼ + α · (y⁽ⁱ⁾ − h_θ(x⁽ⁱ⁾)) · xⱼ⁽ⁱ⁾
LMS / Widrow–Hoff: θⱼ := θⱼ + α(y − h(x))xⱼ.
Update is proportional to the ERROR — large error
⇒ big update; near-zero error ⇒ no update.
This is the gradient of the squared-error loss.
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