If you have a much larger background set you can try online passive
aggressive in mahout 0.6 as it uses hinge loss and does not update the model
of it gets things correct. Log loss will always have a gradient in
contrast.
On Jun 12, 2011 7:54 AM, "Joscha Feth" <jos...@feth.com> wrote:
> Hi Ted,
>
> I see. Only for the OLR or also for any other algorithm? What if my
> other category theoretically contains an infinite number of samples?
>
> Cheers,
> Joscha
>
> Am 12.06.2011 um 15:08 schrieb Ted Dunning <ted.dunn...@gmail.com>:
>
>> Joscha,
>>
>> There is no implicit training. you need to give negative examples as
>> well as positive.
>>
>>
>> On Sat, Jun 11, 2011 at 9:08 AM, Joscha Feth <jos...@feth.com> wrote:
>>> Hello Ted,
>>>
>>> thanks for your response!
>>> What I wanted to accomplish is actually quite simple in theory: I have
some
>>> sentences which have things in common (like some similar words for
example).
>>> I want to train my model with these example sentences I have. Once it is
>>> trained I want to give an unknown sentence to my classifier and would
like
>>> to get back a percentage to which the unknown sentence is similar to the
>>> sentences I trained my model with. So basically I have two categories
>>> (sentence is similar and sentence is not similar). To my understanding
it
>>> does only make sense to train my model with the positives (e.g. the
sample
>>> sentences) and put them all into the same category (I chose category 0,
>>> because the .classifyScalar() method seems to return the probability for
the
>>> first category, e.g. category 0). All other sentences are implicitly
(but
>>> not trained) in the second category (category 1).
>>>
>>> Does that make sense or am I completely off here?
>>>
>>> Kind regards,
>>> Joscha Feth
>>>
>>> On Sat, Jun 11, 2011 at 03:46, Ted Dunning <ted.dunn...@gmail.com>
wrote:
>>>>
>>>> The target variable here is always zero.
>>>>
>>>> Shouldn't it vary?
>>>>
>>>> On Fri, Jun 10, 2011 at 9:54 AM, Joscha Feth <jos...@feth.com> wrote:
>>>>> algorithm.train(0, generateVector(animal));
>>>>>
>>>
>>>
