If I understand the SequenceClassificationModel interface correctly, the input data to be classified is passed as an array T[].
What about data that is very large? I think it would be nice if the new interface would support sequence classifications on streams, e.g. by passing an Iterator<T> or an actual stream to the classifier. Just my 2 cents. Cheers, -- Richard On 29.01.2014, at 19:22, Jörn Kottmann <[email protected]> wrote: > On 01/20/2014 10:48 AM, Jörn Kottmann wrote: >> >> On the training side we already have support for training on sequences. >> Anyway the current implementation is a bit >> unlucky because the sequence training class can only return an Event >> Classification Model. I will change that so that >> a Sequence Classification Model has to be returned, and the Perceptron >> Sequence Model will be returned as a >> Sequence Classification Model instead. > > After looking further into things it seems good to support all three cases: > - A Classification Model trained on non-sequential events > - A Classification Model trained on sequential events > - A Sequence Classification Model trained on sequential events > > Jörn
