# Pooling

### From Ufldl

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Formally, after obtaining our convolved features as described earlier, we decide the size of the region, say <math>m \times n</math> to pool our convolved features over. Then, we divide our convolved features into disjoint <math>m \times n</math> regions, and take the mean (or maximum) feature activation over these regions to obtain the pooled convolved features. These pooled features can then be used for classification. | Formally, after obtaining our convolved features as described earlier, we decide the size of the region, say <math>m \times n</math> to pool our convolved features over. Then, we divide our convolved features into disjoint <math>m \times n</math> regions, and take the mean (or maximum) feature activation over these regions to obtain the pooled convolved features. These pooled features can then be used for classification. | ||

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