|
Efficient Learning with Partially Observed Attributes
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz and Ohad Shamir
In: ICML 2010(2010).
AbstractWe describe and analyze ecient algorithms
for learning a linear predictor from examples
when the learner can only view a few attributes
of each training example. This is the
case, for instance, in medical research, where
each patient participating in the experiment
is only willing to go through a small number
of tests. Our analysis bounds the number
of additional examples sucient to compensate
for the lack of full information on each
training example. We demonstrate the ef-
ciency of our algorithms by showing that
when running on digit recognition data, they
obtain a high prediction accuracy even when
the learner gets to see only four pixels of each
image. [Edit]
|
