00846nam1 2200313 450 99000611251020331620160118113944.0000611251USA01000611251(ALEPH)000611251USA0100061125120160118d--------km-y0itay50------baitaIT||||||||001yy<<Le>> opere di Giorgio ArcoleoMilanoMondadorivolumi23 cm0019900061125302033162001 <<1:>> Studii e profiliArcoleo,GiorgioBNCF320.092ITsalbcISBD990006112510203316XV.9.M. 2358/MARXV.9.M.BKMARIANNONE9020160118USA011139Opere di Giorgio Arcoleo58314UNISA02935nam 22005535 450 991052006700332120251202141804.03-030-88132-610.1007/978-3-030-88132-0(MiAaPQ)EBC6838665(Au-PeEL)EBL6838665(CKB)20275215500041(OCoLC)1290841546(PPN)260307300(BIP)82727961(BIP)81403784(DE-He213)978-3-030-88132-0(EXLCZ)992027521550004120211124d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAutomated Machine Learning and Meta-Learning for Multimedia /by Wenwu Zhu, Xin Wang1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (240 pages)Print version: Zhu, Wenwu Automated Machine Learning and Meta-Learning for Multimedia Cham : Springer International Publishing AG,c2021 9783030881313 Automated Machine Learning -- Meta-learning -- Automated Machine Learning for Multimedia -- Meta-learning for Multimedia -- Future Research Directions.This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.Multimedia systemsMachine learningMultimedia Information SystemsMachine LearningMultimedia systems.Machine learning.Multimedia Information Systems.Machine Learning.006.31Zhu Wenwu763557Wang XinMiAaPQMiAaPQMiAaPQBOOK9910520067003321Automated machine learning and meta-learning for multimedia2909964UNINA