|
|
|
|
|
|
by oergiR
3488 days ago
|
|
|
The "model" in the title is the model of the world, as a probabilistic model. The good thing about such a model is that it explicitly states your beliefs about the world. Once you've defined it, in theory reasoning about it is straightforward. (In practice a lot of papers get written about how to do approximate inference.) It's also straightforward to do unsupervised learning. This is a different perspective from (most uses of) neural networks, which do not have this clear separation between the model and how to reason about it. It's funny that Chris Bishop in 1995 wrote the textbook "Neural Networks for Pattern Recognition" and now is effectively arguing against using neural networks. You can use both by using neural networks as "factors" (the black squares) in probabilistic models. |
|
|
I haven't read "Neural Networks for Pattern Recognition", but his "Pattern Recognition and Machine Learning"[1] is the text for ML work including Bayesian approaches.
I don't think one should view this as "arguing against" neural networks - it's more that Bayesian approaches give you something different.
[1] http://www.springer.com/gp/book/9780387310732