Body: <p>Is Hopfield network more efficient than a naive implementation of Hamming distance that compare an input pattern and return the nearest pattern ?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-07-23T07:31:05.827Z
Comments.Text: "and return the nearest pattern", return the nearest pattern from where? The memory? A database? The Hamming distance is a value that represents the dissimilarity between two strings (at most, it is an algorithm to compute such value), whereas the Hopfield network is a model. These are two apparently completely different concepts. So, why are you even trying to compare them?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-07-23T08:03:33.870Z
Comments.Text: From a database which contains all the vectors of 1 and -1 possible. Yes, this is why I wrote "a naive implementation of Hamming distance that compare patterns" and not just "Hamming distance". So I am not trying to compare a (metrical) distance with a network, but the algorithm that implements brutally / naively comparisons (which criteria is the Hamming distance) of an input pattern with all the patterns that we want to retrieve.
Comments.UserDisplayName: lolodino77
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-07-23T08:07:59.217Z
Comments.Text: For example, imagine we stored two vectors (1 1 1) and (-1 -1 -1) in database and when we give an input like (1 1 -1) we want the algorithm to return (1 1 -1) after having him compare the input (1 1 -1) with (1 1 1) and with (-1 -1 -1) and find that the input (1 1 -1) is closer to (1 1 1) than to (-1 -1 -1).
Comments.UserDisplayName: lolodino77
Tags: <neural-networks>
Title: Is Hopfield network more efficient than a naive implementation of Hamming distance comparator?
UserDisplayName: lolodino77
ThreadText: # Is Hopfield network more efficient than a naive implementation of Hamming distance comparator?
  
  (Asked by: username_0 on 2019-07-22)
  
  Is Hopfield network more efficient than a naive implementation of Hamming distance that compare an input pattern and return the nearest pattern ?
  
  **Tags:** neural-networks
  > **Comments:**
  > **username_1**: "and return the nearest pattern", return the nearest pattern from where? The memory? A database? The Hamming distance is a value that represents the dissimilarity between two strings (at most, it is an algorithm to compute such value), whereas the Hopfield network is a model. These are two apparently completely different concepts. So, why are you even trying to compare them?  
  > **username_0**: From a database which contains all the vectors of 1 and -1 possible. Yes, this is why I wrote "a naive implementation of Hamming distance that compare patterns" and not just "Hamming distance". So I am not trying to compare a (metrical) distance with a network, but the algorithm that implements brutally / naively comparisons (which criteria is the Hamming distance) of an input pattern with all the patterns that we want to retrieve.  
  > **username_0**: For example, imagine we stored two vectors (1 1 1) and (-1 -1 -1) in database and when we give an input like (1 1 -1) we want the algorithm to return (1 1 -1) after having him compare the input (1 1 -1) with (1 1 1) and with (-1 -1 -1) and find that the input (1 1 -1) is closer to (1 1 1) than to (-1 -1 -1).  
  
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  ## 0 answers
  
  