Metalearning : Applications to Automated Machine Learning and Data Mining / by Pavel Brazdil, Jan N. van Rijn, Carlos Soares, Joaquin Vanschoren.

Format:

Book

Author/Creator:

Brazdil, Pavel., Author.

van Rijn, Jan N., Author.

Soares, Carlos, Author.

Vanschoren, Joaquin, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Cognitive technologies 2197-6635

Cognitive Technologies, 2197-6635

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Machine learning.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Machine Learning.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Machine Learning.

Physical Description:

1 online resource (XII, 346 pages) : 90 illustrations, 45 illustrations in color.

Edition:

2nd ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

This open access book offers a comprehensive and thorough introduction to almost all aspects of metalearning and automated machine learning (AutoML), covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. As one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, AutoML is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence.

Contents:

Introduction

Part I, Basic Architecture of Metalearning and AutoML Systems

Metalearning Approaches for Algorithm Selection I

Evaluating Recommendations of Metalearning / AutoML Systems

Metalearning Approaches for Algorithm Selection II

Automating Machine Learning (AutoML) and Algorithm Configuration

Dataset Characteristics (Metafeatures)

Automating the Workflow / Pipeline Design

Part II, Extending the Architecture of Metalearning and AutoML Systems

Setting Up Configuration Spaces and Experiments

Using Metalearning in the Construction of Ensembles

Algorithm Recommendation for Data Streams

Transfer of Metamodels Across Tasks

Automating Data Science

Automating the Design of Complex Systems

Repositories of Experimental Results (OpenML)

Learning from Metadata in Repositories.

Other Format:

Printed edition:

ISBN:

978-3-030-67024-5

9783030670245

Access Restriction:

Restricted for use by site license.