w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight, can this model be used for perceptron learning?(a) yes(b) noI have been asked this question in semester exam.My doubt stems from Pattern Classification in section Feedforward Neural Networks of Neural Networks

w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output,

1.	w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight, can this model be used for perceptron learning?(a) yes(b) noI have been asked this question in semester exam.My doubt stems from Pattern Classification in section Feedforward Neural Networks of Neural Networks
Answer» The CORRECT OPTION is (a) yes Easiest explanation: GRADIENT DESCENT can be used as perceptron LEARNING.

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