Although SVMs and layered neural nets have similar expressivity, the similarity is very much like turing completeness. i.e. Can't tell aparts the haskells from the unlambdas. SVMs express certain functions in a manner that grows exponentially with input vs a deep learner which tends to be more compact. The key to being a deep learner is in using unsupervised learning to seed a hierarchy of learners learning ever more abstract representations.
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Although SVMs and layered neural nets have similar expressivity, the similarity is very much like turing completeness. i.e. Can't tell aparts the haskells from the unlambdas. SVMs express certain functions in a manner that grows exponentially with input vs a deep learner which tends to be more compact. The key to being a deep learner is in using unsupervised learning to seed a hierarchy of learners learning ever more abstract representations.
Also, Multilayered Kernel learners already exist.
"The key to being a deep learner is in using unsupervised learning to seed ..."
Exactly! that was my whole point which doesn't makes sense now that the title had changed.
"Also, Multilayered Kernel learners already exist"
I didn't know that and I'll check that shortly, thanks for the info.