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

This open access book as one of the fastest-growing areas of research in machine learning,
metalearning studies principled methods to obtain efficient models and solutions by …

Current data mining tools are characterized by a plethora of algorithms but a lack of
guidelines to select the right method according to the nature of the problem under analysis …

In this paper, wedescribe a general approach to scaling data mining applications thatwe
have come to call meta-learning. Meta-Learningrefers to a general strategy that seeks to …

In the early days of pattern recognition and statistical data analysis life was rather simple:
datasets were relatively small, collected from well-designed experiments, analyzed using a …

Given a learning task, the standard approach is to experiment with a broad range of
algorithms and parameter settings, and select the model which performs best according to …

Data mining systems aim to discover patterns and extract useful information from facts
recorded in databases. A widely adopted approach to this objective is to apply various …

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 …

In this paper, we describe the JAM system, a distributed, scalable and portable agent-based
data mining system that employs a general approach to scaling data mining applications …

This paper presents new measures, based on the induced decision tree, to characterise
datasets for meta-learning in order to select appropriate learning algorithms. The main idea …