...or you can try:
http://translate.google.com/translate?u=http%3A%2F%2Frogerdai16.wordpress.com%2F2011%2F08%2F20%2F%25E6%2589%2593%25E5%258C%2585mahout%2Fsl=zh-CNtl=enhl=ie=UTF-8
or use Chrome to do it on the fly.
On Sat, Oct 8, 2011 at 9:16 AM, 戴清灏 rogerda...@gmail.com wrote:
If you can
I haven't seen any discussion on it. Do you have a paper or other reference on
it? That usually helps in discussing how to go about it. We have HMM for
classification.
On Oct 7, 2011, at 10:33 PM, Colin wrote:
I haven't find any MC-based open source recommender.
Does Mahout have any plan
Here is one reference which I have used earlier for a class project:
http://jmlr.csail.mit.edu/papers/volume6/shani05a/shani05a.pdf
One can think of recommendation as an instance of sequential optimal
decision making problem, something which MDPs have been traditionally used
for.
Given the
Hi
since taste-web has been migrated to integration folder, how do i compile
and run taste web application using the new place ? Sorry I dont know a lot
about Java. Please help me.
Weide
This doesn't exist in the same form it used to; it will take a bit
more work. You need to compile Mahout, and get the core and
integration .jar files. Make a web application, including these in
WEB-INF/lib. You can use the web.xml file you find under integration
for the web app too, at
On Sat, Oct 8, 2011 at 12:43 PM, Dan Brickley dan...@danbri.org wrote:
...
...and I get as expected, a few less than 100 due to the cleaning (88).
Each
of these has 27683 values, which is the number of topic codes in my data.
I'm reading this (correct me if I have this backwards) as if my
On 8 October 2011 23:58, Ted Dunning ted.dunn...@gmail.com wrote:
On Sat, Oct 8, 2011 at 12:43 PM, Dan Brickley dan...@danbri.org wrote:
...
...and I get as expected, a few less than 100 due to the cleaning (88).
Each
of these has 27683 values, which is the number of topic codes in my data.
On Sat, Oct 8, 2011 at 4:11 PM, Dan Brickley dan...@danbri.org wrote:
Also, while you are at it, I think that the code in MAHOUT-792 might be
able
to do these decompositions at your scale much, much faster since they use
an
in-memory algorithm on a single machine to avoid all the
While reviewing Decision Forest code, I noticed that computing the out of
bag error (OOB) of the forest while training it made the implementation
really messy. I made a lot of assumptions about the way Hadoop works
internally (especially the way it splits the data), this proven many times
to be
