Nonlinear Neural Nets Smooth Wi-Fi Packets 204
mindless4210 writes "Smart Packets, Inc has developed the Smart WiFi Algorithm, a packet sizing technology which can predict the near future of network conditions based on the recent past. The development was originally started to enable smooth real-time data delivery for applications such as streaming video, but when tested on 802.11b networks it was shown to increase data throughput by 100%. The technology can be applied at the application level, the operating system level, or at the firmware level."
Im no programmer, but... (Score:1, Interesting)
neural net (Score:1, Interesting)
Hahaha (Score:3, Interesting)
Re:Im no programmer, but... (Score:5, Interesting)
Prisoner's Dilemma applied to networks flows (Score:4, Interesting)
Re:Why Neural Networks? (Score:2, Interesting)
I hear all too often from people in the field of machine learning who get their favourite solution (SVMs and NNs are the most common) and then they go hunting for a problem.
It might not be exactly the best technique, but if at the time it was the easiest to understand and use, and gave really good results, then the right decision was made.
Is that the difference between theory and practice right there?
Re:Why Neural Networks? (Score:5, Interesting)
Well, it's quite obviously because a Support Vector Machine is inherently linear, and to make it nonlinear, you must insert a nonlinear kernel which you need to select by hand.
Not true [warf.org].
"This invention provides a selection technique, which makes use of a fast Newton method, to produce a reduced set of input features for linear SVM classifiers or a reduced set of kernel functions for non-linear SVM classifiers."
Re:What? (Score:4, Interesting)
There are online methods using both the techniques you mention. The theory is usually a little more involved, so you're not likely to get a good tutorial from page 1 of google results.
Try MIT's open courseware (Machine Learning course) [mit.edu] for some better explanations of this stuff, if you can handle the maths, ughhh.
Re:Why Neural Networks? (Score:5, Interesting)
I figure the real reasons they use NNs are much simpler. Firstly, its really easy to implement NNs that predict numeric values instead of classes and even more importantly they work. Research usually involves trying everything under the sun and reporting/patenting/exploiting whatever worked best.
Re:could be handy.. (Score:5, Interesting)
In other words, bandwidth will do you zero good with a traditional tcp/ip stack if your latency is too high.
Re:which end? (Score:3, Interesting)
Re:OT: Does anyone know if they use this stuff for (Score:1, Interesting)
he's at another job now in a different state, so the real answer is no, there are no commercially viable Holy Grails of Day Trading.
Looks sketchy to me (Score:2, Interesting)
I'm pretty sure that's not the case. Besides, if the technology you're pushing boils down to 'variable-sizing', seems like someone's thought of that before.
As far as neural networks are concerned, a sufficiently complex neural network can adapt to learn reasonably complicated functions. Backpropagation networks and radial basis function networks can make good filters and make sense of noisy data. A network that doesn't adapt its structure boils down to a few matrix operations, so it's easy to script in Matlab.
With all that in mind, shouldn't they have looked at Kalman filters?
not really (Score:3, Interesting)