* Jim Snow [EMAIL PROTECTED] [080531 15:23]:
Without looking at the code to verify that this is how it has really
been implemented, a bloom filter is like a series of hash tables, where
the hash table entries are one bit. The bit is set if there is an item
that hashes to that value in
On Sat, May 31, 2008 at 6:22 PM, Jim Snow [EMAIL PROTECTED] wrote:
In practice, one might use something like 32 hash tables. This yields a
false positive rate of 1/(2^32). Their most obvious application is to store
the dictionary for a spell checker in a space-efficient way, though I have a
G'day all.
Quoting Achim Schneider [EMAIL PROTECTED]:
Please tell me that this isn't reversible.
It isn't reversible.
Cheers,
Andrew Bromage
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On 2008-05-30, Achim Schneider [EMAIL PROTECTED] wrote:
Bryan O'Sullivan [EMAIL PROTECTED] wrote:
A Bloom filter is a probabilistic data
structure that provides a fast set membership querying capability.
It does not give false negatives, but has a tunable false positive
rate. (A false
Aaron Denney [EMAIL PROTECTED] wrote:
On 2008-05-30, Achim Schneider [EMAIL PROTECTED] wrote:
Bryan O'Sullivan [EMAIL PROTECTED] wrote:
A Bloom filter is a probabilistic data
structure that provides a fast set membership querying capability.
It does not give false negatives, but has a
Achim Schneider wrote:
Aaron Denney [EMAIL PROTECTED] wrote:
On 2008-05-30, Achim Schneider [EMAIL PROTECTED] wrote:
Bryan O'Sullivan [EMAIL PROTECTED] wrote:
A Bloom filter is a probabilistic data
structure that provides a fast set membership querying capability.
It does not
Bryan O'Sullivan [EMAIL PROTECTED] wrote:
A Bloom filter is a probabilistic data
structure that provides a fast set membership querying capability.
It does not give false negatives, but has a tunable false positive
rate. (A false positive arises when the filter claims that an
element is
