Automated Machine Learning (AutoML) supports practitioners and researchers with the
tedious task of designing machine learning pipelines and has recently achieved substantial …

Meta-learning, or learning to learn, is the science of systematically observing how different
machine learning approaches perform on a wide range of learning tasks, and then learning …

Metalearning is the study of principled methods that exploit metaknowledge to obtain
efficient models and solutions by adapting machine learning and data mining processes …

Automated Machine Learning, which supports practitioners and researchers with the tedious
task of manually designing machine learning pipelines, has recently achieved substantial …

We present a novel framework that applies a meta-learning approach to clustering
algorithms. Given a dataset, our meta-learning approach provides a ranking for the …

This chapter describes a principled approach to meta-learning that has three distinctive
features. First, whereas most previous work on meta-learning focused exclusively on the …

Meta-learning, a concept from the area of automated machine learning, aims at providing
decision support for data scientists by recommending a suitable setting (a machine learning …

Research in machine learning and data mining can be speeded up tremendously by moving
empirical research results out of people's heads and labs, onto the network and into tools …

This chapter discusses dataset characteristics that play a crucial role in many metalearning
systems. Typically, they help to restrict the search in a given configuration space. The basic …

In this work, we proposed the use of Support Vector Machines (SVM) to predict the
performance of machine learning algorithms based on features of the learning problems …