I have seen the implementation of L-LDA using Java,
Stanford Topic Modeling Toolbox http://nlp.stanford.edu/software/tmt/
Does any one know whether they provide the source code or not?
Thanks,
Maxim
On Fri, Oct 16, 2009 at 12:39 PM, David Hall d...@cs.berkeley.edu wrote:
Sorry, this slipped out
On Fri, Oct 16, 2009 at 4:08 AM, zhao zhendong zhaozhend...@gmail.com wrote:
I have seen the implementation of L-LDA using Java,
Stanford Topic Modeling Toolbox http://nlp.stanford.edu/software/tmt/
Does any one know whether they provide the source code or not?
I'm pretty sure it's scala, no?
Sorry, this slipped out of my inbox and I just found it!
On Thu, Oct 8, 2009 at 12:05 PM, Robin Anil robin.a...@gmail.com wrote:
Posting to the dev list.
Great Paper Thanks!. Looks like L-LDA could be used to create some
interesting examples.
Thanks!
The Paper shows L-LDA could be used to
Posting to the dev list.
Great Paper Thanks!. Looks like L-LDA could be used to create some
interesting examples.
The Paper shows L-LDA could be used to creating word-tag model for accurate
tag(s) prediction given a document of words. I will complete reading and
tell
How much work is need to
On Tue, 22 Sep 2009 14:43:03 -0400
zaki rahaman zaki.raha...@gmail.com wrote:
Sounds good, I'd love to take a look at an outline. I too would love
to see a cookbook style manual which focuses more on the details of
implementation, how to optimize systems, best practices, etc. and
fills in
As I mentioned to some of you, there's a proposal to begin work on a
book on Mahout. It sounds early, but the publisher assures me it's
about the right time to begin, if we want a book out at roughly the
time '1.0' rolls out in a year or so. I've heard support for the idea,
and think it's a good
the
only one who feels this way, but any Mahout book should have some basic
introductory background material -- some discussion about machine learning
(classification, clustering), high level overviews of algorithms, and maybe
some case studies/examples (why use mahout vs. other tools?). And of course
used to the ins and outs
of using Mahout (I've made some hacks to source in my own environment and
have done some testing, but nothing in production yet) but I'd love to help
out on a book, maybe with some of the background material. Maybe I'm the
only one who feels this way, but any Mahout book
On Tuesday 22 September 2009 18:17:29 Sean Owen wrote:
- Who else might be interested in being a co-author and putting in
significant work?
- Would anyone care to read the proposal before I send it in?
- Would anyone help me, in the short term, draft an outline of the
content of the
I would amend that (again) to clustering, classification and recommendations
at scale. With Hadoop where necessary.
On Tue, Sep 22, 2009 at 9:48 AM, Sean Owen sro...@gmail.com wrote:
I sense some consensus that Mahout v1.0 is primarily clustering,
classification and recommendations at scale
On Sep 22, 2009, at 12:59 PM, Ted Dunning wrote:
I would amend that (again) to clustering, classification and
recommendations
at scale. With Hadoop where necessary.
+1
On Tue, Sep 22, 2009 at 9:48 AM, Sean Owen sro...@gmail.com wrote:
I sense some consensus that Mahout v1.0 is
The difference being, not emphasizing Hadoop? I understand that. I
also recall we'd agreed that we were not realistically considering any
other distributed processing framework in the near future, which I
took to mean before v1.0?
On Tue, Sep 22, 2009 at 11:59 AM, Ted Dunning
The difference being that we focus on scalable. This might involve hadoop
for some, all or none of the steps.
My definition of scalable is handles data as big as nearly anybody
produces. That may or may not require Hadoop to do. Many on-line learning
systems are so fast that a single machine
I hope I'm one of the targeted audience members for the book. I've
used hadoop, done clustering (not with Mahout), have read about
collaborative filtering, and plan on using Mahout in a business
intelligence setting in 1-2 years. However, I've never used Mahout
itself. What I would like to see
I could help out with internals of CBayes/Bayes, FPGrowth(if it becomes
ready by then) and writeups or how to's to improve efficiency on different
datasets. how to understand your data and to disable enable various
parameters of CBayes/Bayes to fit non text data. Sparse database v/s dense
+1 for cookbook style. Thats what i meant when i said tuning
CBayes/Bayes(there are around 4-5 knobs which you can modify for fitting you
data perfectly
On Tue, Sep 22, 2009 at 11:15 PM, Tanton Gibbs tanton.gi...@gmail.comwrote:
I hope I'm one of the targeted audience members for the book.
There is certainly no reason to make 'using Hadoop, and nothing else'
a long-term goal. I think there are many reasons to focus on Hadoop in
the short term. And I think this book is about the short term, Mahout
v1.0.
That is I don't disagree -- there's every reason to state the
long-term goal of
That is indeed how I am positioning it in this draft book proposal --
it's for a 'Mahout in Action' book from Manning. They want to
understand why this wouldn't be just another Collective Intelligence
in Action (which I do think is quite a good book, at least, I learned
a good deal about Lucene
Sean,
Sounds good, I'd love to take a look at an outline. I too would love to see
a cookbook style manual which focuses more on the details of implementation,
how to optimize systems, best practices, etc. and fills in with some of the
theory material where appropriate/needed. It wouldn't hurt to
I think that there is a real need for a more general Learning at Scale
book, but I don't think that any of us here are really qualified to write
it.
On Tue, Sep 22, 2009 at 11:00 AM, Sean Owen sro...@gmail.com wrote:
At least, I can't write that theoretical book, and at the
moment, if there is
Hello Sean,
as a Mahout fan I can help with charts, diagrams or schema pictures if
needed. Let's make this book looking real good. Is it true that Manning is
forcing authors to use MS Word? Still it should be possible to use PS, EPS
or maybe PDF for vector graphics, correct?
Anyway, I would love
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