03825nam 22007095 450 991103494320332120251017130414.03-031-99928-210.1007/978-3-031-99928-4(CKB)41665897500041(DE-He213)978-3-031-99928-4(MiAaPQ)EBC32372178(Au-PeEL)EBL32372178(EXLCZ)994166589750004120251017d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Supervised and Semi-supervised Learning Theory and Algorithms /by Massih-Reza Amini1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (XVIII, 309 p. 1 illus.) Cognitive Technologies,2197-66353-031-99927-4 1. Fundamentals of Supervised Learning -- 2. Data-dependent generalization bounds -- 3. Descent direction optimization algorithms -- 4. Deep Learning -- 5. Support Vector Machines -- 6. Boosting -- 7. Semi-supervised Learning -- 8. Learning-To-Rank -- Appendix: Probability reminders.Machine learning is one of the leading areas of artificial intelligence. It concerns the study and development of quantitative models that enable a computer to carry out operations without having been expressly programmed to do so. In this situation, learning is about identifying complex shapes and making intelligent decisions. The challenge in completing this task, given all the available inputs, is that the set of potential decisions is typically quite difficult to enumerate. Machine learning algorithms have been developed with the goal of learning about the problem to be handled based on a collection of limited data from this problem in order to get around this challenge. This textbook presents the scientific foundations of supervised learning theory, the most widespread algorithms developed according to this framework, as well as the semi-supervised and the learning-to-rank frameworks, at a level accessible to master's students. The aim of the book is to provide a coherent presentation linking the theory to the algorithms developed in this field. In addition, this study is not limited to the presentation of these foundations, but it also presents exercises, and is intended for readers who seek to understand the functioning of these models sometimes designated as black boxes.Cognitive Technologies,2197-6635Artificial intelligenceMachine learningInformation storage and retrieval systemsComputer visionPython (Computer program language)StatisticsArtificial IntelligenceMachine LearningInformation Storage and RetrievalComputer VisionPythonBayesian InferenceArtificial intelligence.Machine learning.Information storage and retrieval systems.Computer vision.Python (Computer program language)Statistics.Artificial Intelligence.Machine Learning.Information Storage and Retrieval.Computer Vision.Python.Bayesian Inference.006.3Amini Massih-Rezaauthttp://id.loc.gov/vocabulary/relators/aut1060956MiAaPQMiAaPQMiAaPQBOOK9911034943203321Advanced Supervised and Semi-supervised Learning4449169UNINA01090nam0-2200277 --450 991103668020332120251121191223.020251110d1894----kmuy0itay5050 bafreFRf 001yyFlore coloriée de poche à l'usage du touriste dans les montagnes de la Suisse, de la Savoie, du Dauphiné, des Pyrénées, du Jura, des Vosges, etc.par H. Correvondessins par A. JobinParisLibrairie des Sciences naturelles1894XV, 163 p., 144 p. di tav.ill.16 cmBibliothèque de poche du naturaliste2Flora alpinaAlpiFlora581.918itaCorrevon,Henry<1854-1939>1854029ITUNINAREICATUNIMARCBK9911036680203321E ARB 24803/4736/25FAGBCFAGBCFlore coloriée de poche à l'usage du touriste dans les montagnes de la Suisse, de la Savoie, du Dauphiné, des Pyrénées, du Jura, des Vosges, etc4454320UNINA