Hi Ted,
On Apr 13, 2013, at 8:46pm, Ted Dunning wrote:
On Sat, Apr 13, 2013 at 7:05 AM, Ken Krugler
kkrugler_li...@transpac.comwrote:
On Apr 12, 2013, at 11:55pm, Ted Dunning wrote:
The first thing to try is feature hashing to reduce your feature vector
size.
Unfortunately
Glad to be able to help.
Double hashing would probably allow you to preserve full accuracy at higher
compression, but if you are happy, then you might as well be done.
On Wed, Apr 24, 2013 at 1:56 PM, Ken Krugler kkrugler_li...@transpac.comwrote:
Hi Ted,
On Apr 13, 2013, at 8:46pm, Ted
On Wed, Apr 17, 2013 at 2:29 PM, Ken Krugler kkrugler_li...@transpac.comwrote:
Though I haven't yet found a good write-up on the value of generating more
than one hash - seems like multiple hash values would increase the odds of
collisions.
It does.
But it also increases the chances of
Hi Ted,
On Apr 13, 2013, at 8:46pm, Ted Dunning wrote:
On Sat, Apr 13, 2013 at 7:05 AM, Ken Krugler
kkrugler_li...@transpac.comwrote:
On Apr 12, 2013, at 11:55pm, Ted Dunning wrote:
The first thing to try is feature hashing to reduce your feature vector
size.
Unfortunately
The first thing to try is feature hashing to reduce your feature vector size.
With multiple probes and possibly with random weights you might be able to drop
the size by 10x.
Sent from my iPhone
On Apr 12, 2013, at 18:30, Ken Krugler kkrugler_li...@transpac.com wrote:
Hi all,
We're
On Apr 12, 2013, at 11:55pm, Ted Dunning wrote:
The first thing to try is feature hashing to reduce your feature vector size.
Unfortunately LibLinear takes feature indices directly (assumes they're
sequential ints from 0..n-1), so I don't think feature hashing will help here.
If I
On Sat, Apr 13, 2013 at 7:05 AM, Ken Krugler kkrugler_li...@transpac.comwrote:
On Apr 12, 2013, at 11:55pm, Ted Dunning wrote:
The first thing to try is feature hashing to reduce your feature vector
size.
Unfortunately LibLinear takes feature indices directly (assumes they're
sequential
Hi all,
We're (ab)using LibLinear (linear SVM) as a multi-class classifier, with 200+
labels and 400K features.
This results in a model that's 800MB, which is a bit unwieldy. Unfortunately
LibLinear uses a full array of weights (nothing sparse), being a port from the
C version.
I could do
