04009 am 22006973u 450 991033783580332120230125200551.03-030-05318-010.1007/978-3-030-05318-5(CKB)4100000008217382(DE-He213)978-3-030-05318-5(MiAaPQ)EBC5788944(Au-PeEL)EBL5788944(OCoLC)1105039769(oapen)https://directory.doabooks.org/handle/20.500.12854/31379(PPN)236524313(EXLCZ)99410000000821738220190517d2019 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAutomated Machine Learning[electronic resource] Methods, Systems, Challenges /edited by Frank Hutter, Lars Kotthoff, Joaquin Vanschoren1st ed. 2019.ChamSpringer Nature2019Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (XIV, 219 p. 54 illus., 45 illus. in color.)The Springer Series on Challenges in Machine Learning,2520-131X3-030-05317-2 1 Hyperparameter Optimization -- 2 Meta-Learning -- 3 Neural Architecture Search -- 4 Auto-WEKA -- 5 Hyperopt-Sklearn -- 6 Auto-sklearn -- 7 Towards Automatically-Tuned Deep Neural Networks -- 8 TPOT -- 9 The Automatic Statistician -- 10 AutoML Challenges.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.The Springer Series on Challenges in Machine Learning,2520-131XArtificial intelligenceOptical data processingPattern recognitionArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Image Processing and Computer Visionhttps://scigraph.springernature.com/ontologies/product-market-codes/I22021Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XComputer scienceArtificial intelligenceOptical data processingPattern recognitionArtificial intelligence.Optical data processing.Pattern recognition.Artificial Intelligence.Image Processing and Computer Vision.Pattern Recognition.006.3Hutter Frankedt1356285Hutter Frankedthttp://id.loc.gov/vocabulary/relators/edtKotthoff Larsedthttp://id.loc.gov/vocabulary/relators/edtVanschoren Joaquinedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910337835803321Automated Machine Learning3360613UNINA