Hello:
Hi, Eric. I just read part of your code. It seems it is RBF network
with SDR implement. But the result of classification is good. a
question about the Continuous HTM. Does it learn from dynamic data?
the original HTMCLA learns from the steam data, a series of sequences.
.
On Sun, 4 Jan 2015 00:39:47 -0500
Eric Laukien <[email protected]> wrote:
The code is available here:
https://github.com/222464/AILib/tree/master/Source/rbf
It's called SDRRBFNetwork, but it works just like HTM.
The plankton example is in Source/Kaggle.cpp, the MNIST example is
in
Source/Main.cpp at the very end of the file.
On Sun, Jan 4, 2015 at 12:29 AM, Michael Klachko
<[email protected]>
wrote:
Very interesting! Can you please share your code, and any
instructions on
how to run it, so I could recreate this result? Thanks!
On Sat, Jan 3, 2015 at 9:12 PM, Eric Laukien <[email protected]>
wrote:
I just ran it, I got 95% accuracy after training for 15 minutes on a
single CPU core.
On Sat, Jan 3, 2015 at 10:49 PM, Michael Klachko <
[email protected]> wrote:
Hey Eric, have you tried your implementation on MNIST or CIFAR? If
not,
can you please do so and post the results? I remember someone here
mentioned he got 60% on MNIST with his version of HTM.
On Sat, Jan 3, 2015 at 12:06 AM, Eric Laukien
<[email protected]>
wrote:
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]
