project:hillclimb_dp:draft
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project:hillclimb_dp:draft [2014/05/02 13:43] taolei [Justification of the Algorithm] |
project:hillclimb_dp:draft [2014/05/02 14:57] (current) taolei [Justification of the Algorithm] |
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| Unfortunately, it is not clear if there is an efficient way to compute this loss function by finding $y$ and $y'$. Instead, we could use the standard structural learning loss function, which corresponds to condition (1): | Unfortunately, it is not clear if there is an efficient way to compute this loss function by finding $y$ and $y'$. Instead, we could use the standard structural learning loss function, which corresponds to condition (1): | ||
| $$ | $$ | ||
| - | \mathcal{L}(\hat{x_i},\hat{y_i}) = S(\hat{x_i}, \tilde{y}) + (\tilde{y}) - S(\hat{x_i}, \hat{y_i}) \quad\quad (1o) | + | \mathcal{L}(\hat{x_i},\hat{y_i}) = \max_{\tilde{y}}\left\lbrace S(\hat{x_i}, \tilde{y}) + \text{err}(\tilde{y})\right\rbrace - S(\hat{x_i}, \hat{y_i}) \quad\quad (1o) |
| $$ | $$ | ||
| where $\tilde{y}$ is the output of the decoding algorithm. | where $\tilde{y}$ is the output of the decoding algorithm. | ||
project/hillclimb_dp/draft.1399052604.txt.gz ยท Last modified: 2014/05/02 13:43 by taolei
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