3 research outputs found
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or inputs---to the so-called class label---or output. The
fields of pattern recognition and machine learning study ways of constructing
such classifiers. The main idea behind supervised methods is that of learning
from examples: given a number of example input-output relations, to what extent
can the general mapping be learned that takes any new and unseen feature vector
to its correct class? This chapter provides a basic introduction to the
underlying ideas of how to come to a supervised classification problem. In
addition, it provides an overview of some specific classification techniques,
delves into the issues of object representation and classifier evaluation, and
(very) briefly covers some variations on the basic supervised classification
task that may also be of interest to the practitioner
The Shape of Learning Curves: a Review
Learning curves provide insight into the dependence of a learner's
generalization performance on the training set size. This important tool can be
used for model selection, to predict the effect of more training data, and to
reduce the computational complexity of model training and hyperparameter
tuning. This review recounts the origins of the term, provides a formal
definition of the learning curve, and briefly covers basics such as its
estimation. Our main contribution is a comprehensive overview of the literature
regarding the shape of learning curves. We discuss empirical and theoretical
evidence that supports well-behaved curves that often have the shape of a power
law or an exponential. We consider the learning curves of Gaussian processes,
the complex shapes they can display, and the factors influencing them. We draw
specific attention to examples of learning curves that are ill-behaved, showing
worse learning performance with more training data. To wrap up, we point out
various open problems that warrant deeper empirical and theoretical
investigation. All in all, our review underscores that learning curves are
surprisingly diverse and no universal model can be identified