On Mon, May 23, 2011 at 5:43 PM, Benson Margulies <[email protected]>wrote:
> On Mon, May 23, 2011 at 8:34 PM, Daniel McEnnis <[email protected]> > wrote: > > The traditional meaning of feature in machine learning as I understand > > it is an arbitrary piece of information about some object. These > > features are usually grouped by type into a feature vector which > > provides a uniform way to describe of any object of the same class. > > Except when they aren't. Consider a sequence tagger. There are > features, and no vectors. > > > The features are still conventionally grouped into an array of features called a feature vector. For a sequence tagger, there is a feature vector for each point in the sequence that needs to be tagged. It can include the neighboring sequence elements, neighboring model generated tags and the phase of the moon. The raw feature vector is often reprocessed in various ways to get the vector of values the actually gets passed to the learning algorithm (for training) or the resulting model (for classification). I thought Daniel's description was quite good.
