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 <[email protected]>: > 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 <[email protected]> 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 <[email protected]> wrote: >>> >>> The target variable here is always zero. >>> >>> Shouldn't it vary? >>> >>> On Fri, Jun 10, 2011 at 9:54 AM, Joscha Feth <[email protected]> wrote: >>>> algorithm.train(0, generateVector(animal)); >>>> >> >>
