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钟尚武
dlib
Commits
6758a89b
Commit
6758a89b
authored
Apr 21, 2013
by
Davis King
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updated docs
parent
17ebc2e2
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2 changed files
with
25 additions
and
1 deletion
+25
-1
algorithms.xml
docs/docs/algorithms.xml
+24
-1
term_index.xml
docs/docs/term_index.xml
+1
-0
No files found.
docs/docs/algorithms.xml
View file @
6758a89b
...
@@ -82,6 +82,7 @@
...
@@ -82,6 +82,7 @@
<item>
uniform_random_hash
</item>
<item>
uniform_random_hash
</item>
<item>
projection_hash
</item>
<item>
projection_hash
</item>
<item>
create_random_projection_hash
</item>
<item>
create_random_projection_hash
</item>
<item>
create_max_margin_projection_hash
</item>
<item>
hash_samples
</item>
<item>
hash_samples
</item>
<item>
hash_similar_angles_64
</item>
<item>
hash_similar_angles_64
</item>
<item>
hash_similar_angles_128
</item>
<item>
hash_similar_angles_128
</item>
...
@@ -352,10 +353,32 @@
...
@@ -352,10 +353,32 @@
<component>
<component>
<name>
create_random_projection_hash
</name>
<name>
create_random_projection_hash
</name>
<file>
dlib/lsh.h
</file>
<file>
dlib/lsh.h
</file>
<spec_file>
dlib/lsh/create_random_projection_hash_abstract.h
</spec_file>
<spec_file
link=
"true"
>
dlib/lsh/create_random_projection_hash_abstract.h
</spec_file>
<description>
Creates a random projection based locality sensitive
<a
href=
"#projection_hash"
>
hashing function
</a>
. The projection matrix
is generated by sampling its elements from a Gaussian random number generator.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>
create_max_margin_projection_hash
</name>
<file>
dlib/lsh.h
</file>
<spec_file
link=
"true"
>
dlib/lsh/create_random_projection_hash_abstract.h
</spec_file>
<description>
<description>
Creates a random projection based locality sensitive
Creates a random projection based locality sensitive
<a
href=
"#projection_hash"
>
hashing function
</a>
.
<a
href=
"#projection_hash"
>
hashing function
</a>
.
This is accomplished using a variation on the random hyperplane generation
technique from the paper:
<blockquote>
Random Maximum Margin Hashing by Alexis Joly and Olivier Buisson
</blockquote>
In particular, we use a linear support vector machine to generate planes.
We train it on randomly selected and randomly labeled points from
the data to be hashed.
</description>
</description>
</component>
</component>
...
...
docs/docs/term_index.xml
View file @
6758a89b
...
@@ -165,6 +165,7 @@
...
@@ -165,6 +165,7 @@
<term
file=
"algorithms.html"
name=
"hash_similar_angles_512"
include=
"dlib/lsh.h"
/>
<term
file=
"algorithms.html"
name=
"hash_similar_angles_512"
include=
"dlib/lsh.h"
/>
<term
file=
"algorithms.html"
name=
"projection_hash"
include=
"dlib/lsh.h"
/>
<term
file=
"algorithms.html"
name=
"projection_hash"
include=
"dlib/lsh.h"
/>
<term
file=
"algorithms.html"
name=
"create_random_projection_hash"
include=
"dlib/lsh.h"
/>
<term
file=
"algorithms.html"
name=
"create_random_projection_hash"
include=
"dlib/lsh.h"
/>
<term
file=
"algorithms.html"
name=
"create_max_margin_projection_hash"
include=
"dlib/lsh.h"
/>
<term
file=
"imaging.html"
name=
"randomly_sample_image_features"
include=
"dlib/statistics.h"
/>
<term
file=
"imaging.html"
name=
"randomly_sample_image_features"
include=
"dlib/statistics.h"
/>
<term
file=
"algorithms.html"
name=
"disjoint_subsets"
include=
"dlib/disjoint_subsets.h"
/>
<term
file=
"algorithms.html"
name=
"disjoint_subsets"
include=
"dlib/disjoint_subsets.h"
/>
<term
link=
"algorithms.html#disjoint_subsets"
name=
"union-find"
include=
"dlib/disjoint_subsets.h"
/>
<term
link=
"algorithms.html#disjoint_subsets"
name=
"union-find"
include=
"dlib/disjoint_subsets.h"
/>
...
...
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