Not so trivially, these classifiers can help each other. What you have is
a form of transduction or example based learnng.
On Fri, May 18, 2012 at 5:24 PM, Sean Owen wrote:
> Trivially it's four classifiers. You have just one input here, and
> it's binary. That seems like too little info to dis
Trivially it's four classifiers. You have just one input here, and
it's binary. That seems like too little info to discriminate on. All
you can learn -- and it doesn't really need a classifier algorithm --
is there's an x% chance of encountering problem a if funded, and
(100-x)% of a if not.
On Fr
Yes it's just like making any other servlet-based app. You can find
the servlet (and Axis JWS file if you want) and web.xml in the
project. Just put it together with compiled code in a .war file.
On Fri, May 18, 2012 at 10:09 PM, Dhananjay Sampath
wrote:
> Thanks Sean, for the super quick respons
Hi,
I suppose this a combination of a generic machine learning question and a
mahout question.
I have a data set. A user may or may not be part of a funded scheme.
If there are not part of the funded scheme they might be susceptible to
certain problems a, b, c and d.
If there are part of the fu
Thanks Sean, for the super quick response!
I wish I asked this question 3 days ago !
Ok, so I have to package my own recommender. Got it. The only place where I
found some instructions on packaging a (custom/example) recommender is
Manuel's response to Ben on the User archives (
http://mail-archiv
Hello,
I'm sure variations of this question have been posted before but I'm having
trouble using my own custom analyzer with seq2sparse. I'm using the -a
parameter to pass my class name.
To build the class I basically cloned the concept in Mahout's
org.apache.mahout.vectorizer.DefaultAnalyz
(You can use -DskipTests in Maven. You don't need to run the very
lengthy tests.)
The bad news is that this example only worked in 0.5, and was removed
in 0.6. The underlying pieces are still there, you just would have to
assemble the WAR yourself.
I'll try to figure out how to remove this; I did
Hi mahout-devs/mahout-users,
I am here stuck on trying to get the mahout taste app demo working and
could really use some help. I know that several people have come before me
asking the same question and I have gone through almost all of them and
have met with little success.
I followed all of th
very helpful info! Thanks a lot.
On Fri, May 18, 2012 at 11:37 AM, John Conwell wrote:
> Noise in OCR often manifests itself as a whole bunch of singletons in the
> corpus of meaningless terms like "lsdjfdslkfj". So the minFrequency flag
> can help in filtering out these terms.
>
> Stopwords sho
Thanks Jeff. When I did my experiment it used kmeans for three runs k =
10, 20, 10. Number of documents around 3000 (guessing here).
The k=10 run did not prune, k=30 pruned 4 clusters. I'll run this again
to see if it is repeatable and you are welcome to the dataset.
I read that comment but w
Noise in OCR often manifests itself as a whole bunch of singletons in the
corpus of meaningless terms like "lsdjfdslkfj". So the minFrequency flag
can help in filtering out these terms.
Stopwords should be handled by tfidf. For example the word "the" probably
has a high frequency in every docume
I'm trying to understand how LDA prints out the words per topic. If I run
the reuters example, the topics are printed out like this:
Topic 0
> ===
> dlrs [p(dlrs|topic_0) = 0.09982075792238235
> mln [p(mln|topic_0) = 0.05160370562850524
> its [p(its|topic_0) = 0.026424106119119467
> earni
Thanks for the quick reply.
Stop word filtering or stemming may not help much I think. Too, the
point of using tf-idf vector is to deal with high occurrence frequency
word. Stop word filtering or stemming seems counter against the tf-idf
intention. The problem is that the text has lots of noises (
In addition. You could try to increase the word occurance thresholds
in -s and -md options.
On Fri, May 18, 2012 at 9:41 AM, John Conwell wrote:
> What do you have in mind as far as a different tokenizer? Are you doing
> stopword filtering? Maybe look at the stopword list and see if there are
>
What do you have in mind as far as a different tokenizer? Are you doing
stopword filtering? Maybe look at the stopword list and see if there are
other noise words you wish to add. If you are using Lucene to filter
stopwords, its stopword list if pretty small(20 or so words). Stemming is
another
Hi List,
I am trying to use Mahout to do cluster on text. The problem is after
running the procedure SparseVectorsFromSequenceFiles, the dimension of
tf-idf vector is too high (about 50K) and it increases as the number
of document increases. I think there are two ways to handle that. One
is to use
16 matches
Mail list logo
