Automated Machine Learning

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

• Tipo de recurso: Book
• Fecha de publicación: 2019
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• Idioma: eng
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/16781
• Acceso en línea: https://link.springer.com/book/10.1007/978-3-030-05318-5; http://hdl.handle.net/20.500.12010/16781
• Palabra clave: Ciencias de la computación; Inteligencia artificial -- Robótica; Procesamiento óptico de datos; Reconocimiento de patrones
• License: Abierto (Texto Completo)