This open access book presents the first comprehensive overview of general methods in
Automated Machine Learning (AutoML), collects descriptions of existing systems based on …

Automated machine learning (AutoML) is the sub-field of machine learning that aims at
automating, to some extend, all stages of the design of a machine learning system. In the …

Abstract Cross-Validation (CV), and out-of-sample performance-estimation protocols in
general, are often employed both for (a) selecting the optimal combination of algorithms and …

Background. For the evaluation and comparison of markers and risk prediction models,
various novel measures have recently been introduced as alternatives to the commonly …

Abstract The ChaLearn AutoML Challenge (The authors are in alphabetical order of last
name, except the first author who did most of the writing and the second author who …

While the model parameters of a kernel machine are typically given by the solution of a
convex optimisation problem, with a single global optimum, the selection of good values for …

This paper proposes the application of particle swarm optimization (PSO) to the problem of
full model selection, FMS, for classification tasks. FMS is defined as follows: given a pool of …

Forward-backward selection is one of the most basic and commonly-used feature selection
algorithms available. It is also general and conceptually applicable to many different types of …

The principle of parsimony also known as “Ockham's razor” has inspired many theories of
model selection. Yet such theories, all making arguments in favor of parsimony, are based …

Justification Automatic brain tumor classification by MRS has been under development for
more than a decade. Nonetheless, to our knowledge, there are no published evaluations of …