On Mon, Mar 3, 2008 at 6:39 PM, Richard Loosemore <[EMAIL PROTECTED]> wrote:
> The problems with bolting together NN and GA are so numerous it is hard > to know where to begin. For one thing, you cannot represent structured > information with NNs unless you go to some trouble to add extra > architecture. Most NNs can only cope with single concepts learned in > isolation, so if you show a visual field containing 5,000 copies of the > letter 'A', all that happens is that the 'A' neuron fires. > > If you do find some way to get around this problem, your solution will > end up being the tail that wags the dog: the NN itself will fade into > relative insignificance compared to your solution. Well, you could achieve that (5000 registration of the letter 'A' with their corresponding position in the image) by using a sliding window over multiple rescaled (and maybe other transformations) transformations of the input image. This way, you get image patches for each window and scale (and maybe other transformations), and each patch can be a given a corresponding position in multidimensional space (e.g., an image patch with X and Y position and scale S has is a point in 3-dimensional space). For each of the produced points (patches) in the space, run the neural net to produce a lower-dimensional code and corresponding energy (= reconstruction quality). Now filter this space by let the points have local battles for salience using some heuristic (e.g. lower energy means higher salience) and filter out the low-salient points. This produces a filtered space with fewer points then the previous one, and each point containing a lower-dimensional code. In the example of the letter 'A', the above method would recognize all 5000 versions while remembering their individual input position. This presumes the neural net is properly trained on the letter 'A' and can properly reconstuct them (using Hinton's method). This should produce 5000 registrations of the letter 'A', while filtering out unimportant information. But you could take it a step further. For each image input, the above method creates a filtered, 3-dimensional space with points containing low-dimensional codes. This space can then again be harvested by taking patches with each patch containing *n* points, each point containing an *m *dimensional code, so each patch being (*m***n*).* *A neural net can be trained on lowering the dimension of these patches from (*m***n*) to something lower-dimensional. This process is quite similar to the one in the previous paragraph. What could *possibly *go wrong? :) Regards, Durk Kingma > > > > Richard Loosemore > > > >> ----- Original Message ----- > >> From: "Bob Mottram" <[EMAIL PROTECTED]> > >> To: agi@v2.listbox.com > >> Subject: Re: [agi] interesting Google Tech Talk about Neural Nets > >> Date: Mon, 3 Mar 2008 09:48:08 +0000 > >> > >> > >> On 03/03/2008, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote: > >>> Dont you see the way to go on Neural nets is hybrid with genetic > >>> algorithms in mass amounts? > >> > >> I experimented with this combination in the early 1990s, and the > >> results were not very impressive. Such systems still suffered from > >> extremely slow learning and poor scalability. > >> > >> ------------------------------------------- > >> agi > >> Archives: http://www.listbox.com/member/archive/303/=now > >> RSS Feed: http://www.listbox.com/member/archive/rss/303/ > >> Modify Your Subscription: > >> http://www.listbox.com/member/?&; > >> Powered by Listbox: http://www.listbox.com > > > > > > > > ------------------------------------------- > agi > Archives: http://www.listbox.com/member/archive/303/=now > RSS Feed: http://www.listbox.com/member/archive/rss/303/ > Modify Your Subscription: > http://www.listbox.com/member/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=95818715-a78a9b Powered by Listbox: http://www.listbox.com
