I think I agree with Michael. NuPIC is best suited (in its current state) for fast moving temporal data. It seems like your problem is not temporal. --------- Matt Taylor OS Community Flag-Bearer Numenta
On Wed, Feb 11, 2015 at 9:46 AM, Michael Klachko <[email protected]> wrote: > Hi yajingfu, your problem is rather simple, so you can probably get better > results by using traditional ML methods (simple neural net, svm, or even > regression). Why do you want to use HTM? > > On Wed, Feb 11, 2015 at 7:29 AM, 天朗气清 <[email protected]> wrote: >> >> hello all >> I sent an email to you last week. But I'm afraid that I didn't get my >> answer. It seems that I didn't explain my problem clearly, so I try to send >> this email again. >> I'm trying to use HTM to analyse disease data. The data have about 4000 >> lines, and each line have many columns, the first column represent for >> diease D, and the rest columns represent for many symptoms S1 S2 S3...Sn. We >> need do infer what kind of desease D is depending on the symptoms S1 S2 >> S3...Sn. I think it's same with the process of handwritten digits >> recognition: first encoding, then spatial pooler, last classification. So I >> regard one piece of disease data as an image. But I think maybe it is not >> the best way to process my data. So I want to know are there any better >> method to process such multifields category? Are there any example that can >> be used as a reference? >> >> >> Hoping for your reply. >> Best regards. >> yajingfu > >
