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Transcript
âBag of wordsâ kernels (1) ï® ï® ï® Document seen as a vector d, indexed by all the elements of a (controlled) dictionary. The entry is equal to the number of occurrences. A training corpus is therefore represented by a Term-Document matrix, noted D=[d1 d2 ⦠dm-1 dm] From this basic representation, we will apply a sequence of successive embeddings, resulting in a global (valid) kernel with all desired properties 13
