| |
Abstract:
We consider the general problem of learning multi-category
classification from labeled examples. In many practical learning
settings the sample size or time available for training are
limited, which may have adverse effects on the accuracy of the
resulting classifier. To compensate for this, researchers have
considered various ways of trying to make the learning process more
efficient so that even with limited resources high accuracy may be
possible. The classical theory of pattern recognition assumes
labeled examples appear according to unknown underlying class
conditional probability distributions where the pattern classes are
picked randomly in a passive manner according to their a priori
probabilities. In this paper, we present experimental results for
an algorithm which actively selects samples from different pattern
classes according to a querying rule as opposed to the a priori
probabilities. The amount of improvement of this query-based
approach over the passive batch approach depends on the complexity
of the Bayes rule. The query rule essentially learns to tune the
subsample size of each of the pattern classes to this Bayes
complexity which is assumed to be unknown. The principle on which
this algorithm is based is general enough to be used in any
learning algorithm which permits a model-selection criterion and
for which the error rate of the classifier is calculable in terms
of the complexity of the model.
|
