- ... system.
^{1.1}
- It is not essential that the input
be perceptual. However, perceptual modalities are rich, expressive,
intuitive and non-obtrusive. One could take other measurements if they
help infer behaviour, internal state or intentionality.
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- ... game)
^{2.1}
- Of course, the
individuals need not be in the same physical space and could be
interacting through a virtual environment.
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- ... integral
^{5.1}
- Either analytically or by sampling
techniques
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- ... space
^{5.2}
- This is the typical task of unsupervised
learning.
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- ... output
^{5.3}
- This
is the typical task of supervised learning.
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- ... density.
^{5.4}
- A proof by example can be found in the
appendix.
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- ...
optimization.
^{6.1}
- We shall focus on maximization in this
document but most arguments should apply to minimization problems as
well.
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- ...
^{6.2}
- In this sense,
acts like a variational parameter.
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- ...>0
^{6.3}
- Here,
*a* is also only an integer traditionally but this is not necessary
for the following derivation. Also note that *a* does not truly have
to be positive since
can be used to factor out
the negativity.
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- ... covariances
^{7.1}
- In fact, step 1)
and step 2) can be performed simultaneously since they only involves
taking derivatives and setting to 0.
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- ...[5]
^{7.2}
- A classic example of this is that
of dystal learning to recover the angle at which to throw a ball when trying
shoot it a pre-specified distance. Since throwing a ball at 90 or 0
degrees is equivalent (and any other (90-
*x*) or (*x*) combination), the
expected value is to always throw the ball at 45 degrees which is
incorrect and would have probability .
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- ...
constraints
^{11.1}
- In fact, in assuming a joint density, the model is a parent
of the
data instead of being a co-parent with it.
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