Yes. This is what I was saying.
The decision about what is good to have in mahout has most to do with what machine learning related task needs parallelism not whether it is "learning" or "feature selection" or "feature extraction". That said, the mahout project also needs reference implementations of all of these algorithms in sequential form for testing. On 3/21/08 2:08 AM, "Hao Zheng" <[EMAIL PROTECTED]> wrote: > I understand. Actually, I mean the other thing. Maybe "feature > selection" is not precise, let me restate my question. > > Generally, no matter for image recognition or text classification, we > have to ture the original material into a featrue vector. This step is > called "feature extration" or sth like that. My question is will this > step be part of the mahout project? If yes, we have to care about the > transformation step; if not, all we need to process are the numbers, > which will make thing easier. > > On Fri, Mar 21, 2008 at 9:02 AM, Ted Dunning <[EMAIL PROTECTED]> wrote: >> >> I think a better description is that this project is about ML algorithms >> that need large scale. >> >> If you have very inexpensive feature selection that can run sequentially, >> then it probably doesn't matter to use hadoop/mahout for that. Some forms >> of feature extraction is very expensive, however, and could definitely >> benefit from parallelism. For instance, you could imagine that the feature >> extraction step involves a large scale non-deterministic clustering. It >> might even be that the the feature extraction requires parallel processing, >> but the actual learning algorithm does not.
