Inbook,
Scaling Learning Algorithms towards AI
MIT Press, (2007)
Abstract
One long-term goal of machine learning research is to produce methods that
are applicable to highly complex tasks, such as perception (vision, audition), rea-
soning, intelligent control, and other artificially intelligent behaviors. We argue
that in order to progress toward this goal, the Machine Learning community must
endeavor to discover algorithms that can learn highly complex functions, with min-
imal need for prior knowledge, and with minimal human intervention. We present
mathematical and empirical evidence suggesting that many popular approaches
to non-parametric learning, particularly kernel methods, are fundamentally lim-
ited in their ability to learn complex high-dimensional functions. Our analysis
focuses on two problems. First, kernel machines are shallow architectures, in
which one large layer of simple template matchers is followed by a single layer
of trainable coefficients. We argue that shallow architectures can be very ineffi-
cient in terms of required number of computational elements and examples. Sec-
ond, we analyze a limitation of kernel machines with a local kernel, linked to the
curse of dimensionality, that applies to supervised, unsupervised (manifold learn-
ing) and semi-supervised kernel machines. Using empirical results on invariant
image recognition tasks, kernel methods are compared with deep architectures, in
which lower-level features or concepts are progressively combined into more ab-
stract and higher-level representations. We argue that deep architectures have the
potential to generalize in non-local ways, i.e., beyond immediate neighbors, and
that this is crucial in order to make progress on the kind of complex tasks required
for artificial intelligence.
