Hello, The original HTM isn't really suitable for images as far as I know. I developed an extension specifically to handle image information, it is called Continuous HTM. I then made a classifier from this, and applied it to a Kaggle competition. I got great results within just a few minutes on a single CPU core. The competition (still running) is about classifying 123 species of plankton based on images. Here is an image. Keep in mind that 51% accuracy is actually very good on this competition for a first attempt (world record is something like 71%), and I only trained for a few minutes. Let me know if you are interested, I can share the code with you.
On Fri, Jan 2, 2015 at 9:40 PM, <[email protected]> wrote: > Hello. > > I'm a master student of TUAT. Currently, I am doing some research > which is about handwritten character recognition(offline recognition). > and I'm very interested in the HTM. I read the paper "How the Brain > Might Work: A Hierarchical and Temporal Model for Learning and > Recognition" and "Pattern Recognition by Hierarchical Temporal > Memory", which are about the old version of HTM. I think the result > shows in the paper is good, and i want to test the HTM with some other > dataset (Alphabet, maybe more complex dataset like Chinese character), > then I found the old HTM is obsolete. Now i want to test the HTMCLA > with MNIST database (It seems someone already did it, but I didn't > find any paper shows the result). > > I found there is a mnist test on the > github(https://github.com/numenta/nupic.research/blob/ > master/image_test/mnist_test.py). > Then I dumped all images > and labels from MNIST database(http://yann.lecun.com/exdb/mnist/) and > try to use it to see if it works. After fixed some error, the program > could run without any problem. But the result shows that the > KNNClassifier only learned 1 category. > "Num categories learned 1" > The accuracy is lower than 10%. > Any one knows what kind of problem that is. could any one help me? > > Thank you. > > An Qi > Tokyo University of Agriculture and Technology - Nakagawa Laboratory > 2-24-16 Naka-cho, Koganei-shi, Tokyo 184-8588 > [email protected] > > > > > >
