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I'd like to expand upon Anton's comment in his answer, and provide an explicit answer to the situation posed by Ashwin in the comments. I think it'll be helpful to answering the primary question. Let's consider the situation Where A is the starting node and G is the goal node. The numbers on the nodes are the heuristic costs, while the numbers on the edges ...


1

We have that $A \Rightarrow B$ is same with $\neg A \lor B$. This gives, that $(I \lor M)\Rightarrow H$ is same with $\neg(I \lor M)\lor H$. And we have that $\neg(I \lor M)$ is same with $\neg I \land \neg M$ Putting together we have that $(I \lor M)\Rightarrow H $ is same with $\neg I \land \neg M \lor H = (\neg I \land \neg M) \lor H = (\neg I \lor H) \...


0

Regularization will always increase the MSE loss on the training set (training loss). Intuitively, it constraints/limits the set of regression lines you can use. However, the MSE loss on the test set can be either increase (for the same reason) or decrease (if it has helped combat overfitting and helped bias the classifier towards a simpler hypothesis, and ...


-1

Recognising the right face is not the problem. Reliably rejecting the wrong face is.


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