I have 165000 observations. Each observation is 180 component vector.
One component in this vector is an integer in range from 0 to 200.
Every vector is constructed from readings of 180 sensors. Sensors fail
unpredictably, so some vector components may be undefined. On average
40%  60% of random vector components are undefined in every vector.
I am trying to find an approximation function for each component of
these vectors.
What other methods for prediction with incomplete data can be used for
a task I just described?
>From Ted Dunning <ted.dunn...@gmail.com>
>Subject Re: Function approximation in Mahout?
>Date Wed, 21 Apr 2010 14:34:58 GMT
>
>Mahout does not have a lot of regression capabilities at this time, other
>than various forms of binomial regression (SVM, logistic regression,
>decision forests) but other forms of regression are relatively lacking.
>
>Commons math has some capabilities, but not in a particularly scalable form.
>
>What size is your problem?
>
>On Tue, Apr 20, 2010 at 2:07 PM, Dmitri O.Kondratiev <dokondr@gmail.com>wrote:
>
>> Hello,
>> Does Mahout support any function approximation frameworks, such as greedy
>> function approximation with gradient boosting (TreeNet)?
>> http://en.wikipedia.org/wiki/TreeNet#Names
>>
>> Thanks!
>>
