Suppose you want to optimize the mean squared loss (page 270) for the sigmoid of a linear

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Suppose you want to optimize the mean squared loss (page 270)

for the sigmoid of a linear function.

(a) Modify the algorithm of Figure 7.12 (page 292) so that the update is proportional to the gradient of the squared error. Note that this question assumes you know differential calculus, in particular, the chain rule for differentiation.

(b) Does this work better than the algorithm that minimizes log loss when evaluated according to the squared error?

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Artificial Intelligence: Foundations Of Computational Agents

ISBN: 9781009258197

3rd Edition

Authors: David L. Poole , Alan K. Mackworth

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Question Posted: Oct 22, 2024 03:00 AM

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Suppose you want to optimize the mean squared loss (page 270) for the sigmoid of a linear

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Artificial Intelligence: Foundations Of Computational Agents

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