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Gaussian Processes can learn any arbitrary function. Is it "shallow" machine learning? I think the point that the parent makes is valid: Most of the advantages
by halflings 10y ago
Gaussian Processes can learn any arbitrary function. Is it "shallow" machine learning?
I think the point that the parent makes is valid: Most of the advantages of deep learning when using a "simple" feed-forward topology is advances related to scaling learning and solving problems encountered at with difficult tasks like image recognition, etc.
I do not know enough about neural nets to say if that is all there is to it, but one thing is sure: it's not just about "learning features", although it was shown that the output at every layer abstracts some sort of higher-level features (in the case of image recognition)
- Houshalter 10y agoGPs require exponentially many parameters though. They can't learn arbitrary functions, they just stupidly memorize a lookup table.
- YeGoblynQueenne 10y ago>> it's not just about "learning features" So, I'm in no position to prove this, but my intuition is that any machine learning algorithm can be configured in a semi-supervised learning set-up, like deep nets have. You could train a decision forest classifier for instance to learn in an unsupervised manner. An algorithm I'm developing for my MSc dissertation is essentially unsupervised recursive partitioning, a.k.a. decision trees (only, first-order rather than propositional). Well, possibly not _any_ algorithm. But I get the feeling that many classifiers in particular could be adapted to unsupervised learning with a bit of elbow grease, at which point you could connect them to their own input and, voila, semi-supervised learning. But like I say, I don't reckon I'll be in a position to prove this any time soon.