Model of Artificial Neural Networks
It’s been a long time since I last posted on here, but I’ve been meaning to do it for a while. In a previous post I presented some general musings on the topics of location prediction, machine learning, and geographic routing. At the point I was reading Bishop and some other resources to try and get to grips with the fundamentals of machine learning in to try and find something that would hopefully be useful. Well, I finished the book (which is an excellent resource and one I intend to properly review some day) and started experimenting with machine learning algorithms in Matlab. Contrary to what I said previously I decided to focus entirely on continuous regression methods.
There are a few reasons for this change of mind, the first is accuracy. What I am really interested in doing is developing a system that is able to accurately predict future coordinates of a device based on previous information. Now the reason I want to do this is to enhance geographic routing and develop location/context-aware systems for improving QoS. However, more generally I believe that such a system is potentially beneficial to the wider area of context-aware/pervasive computing. Therefore, even if predicting discrete locations (i.e. relative points or just distance) would be enough to improve performance I still want to be able to accurately predict actual physical locations (represented by coordinates). The second reason was more practical and that is that it seems very difficult to provide a general framework for relative locations. If you are developing a system for one specific area then that is not a problem as you have a limited physical space and there the problem is discrete, however this is very hard to generalise. One approach towards generalisation would be the use of a custom-defined grid in which the classes were cells, but this does not seem to me to have the right degree of accuracy and unless the network area is physically fixed the grid would have to expand and alter to cope with movement outside it. The third (which is very closely linked to the second) is that developing appropriate models for location classification would take too long and possibly wouldn’t yield particularly high results, whereas with regression it would be fairly simple to set up and then asses the success.
Source: Fraser Cadger's Research Blog
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