Neural networks are not considered obsolete by the machine learning community; in fact there is much active research on neural networks and the term is understood to be quite general. SVMs are linear classifiers for hand-engineered features. When a single layer of template-matchers isn't enough, neural networks can be quite effective for extracting features that could be a "kernel" for an SVM or whatnot. If anything, neural networks research gets marketed as research on probabilistic graphical models more often than it gets marketed as kernel machines research.
- George On Wed, Oct 14, 2009 at 9:06 AM, Rémi Coulom <remi.cou...@univ-lille3.fr> wrote: > Petr Baudis wrote: >> >> Hi! >> >> Is there some "high-level reason" hypothesised about why there are >> no successful programs using neural networks in Go? >> >> I'd also like to ask if someone has a research tip for some >> interesting Go sub-problem that could make for a nice beginner neural >> networks project. >> >> Thanks, >> > > At the time when it was fashionable, I would have sold my pattern-Elo stuff > as a neural network, because, in neural-network jargon, it is in fact a > one-layer network with a softmax output. Since the development of > support-vector machines, neural networks have been considered completely > obsolete in the machine-learning community. From a marketing point of view, > it is not a good idea to do research on neural networks nowadays. You must > give your system another name. > > Rémi > _______________________________________________ > computer-go mailing list > computer-go@computer-go.org > http://www.computer-go.org/mailman/listinfo/computer-go/ > _______________________________________________ computer-go mailing list computer-go@computer-go.org http://www.computer-go.org/mailman/listinfo/computer-go/
