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024 7 $a10.1007/978-3-031-99928-4
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035   $a(DE-He213)978-3-031-99928-4
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100   $a20251017d2025      u|         0
101 0 $aeng
135   $aur|||||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAdvanced Supervised and Semi-supervised Learning $eTheory and Algorithms /$fby Massih-Reza Amini
205   $a1st ed. 2025.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025.
215   $a1 online resource (XVIII, 309 p. 1 illus.) 
225 1 $aCognitive Technologies,$x2197-6635
311 08$a3-031-99927-4 
327   $a1. 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.
330   $aMachine learning is one of the leading areas of artificial intelligence. It concerns the study and development of quantitative models that enable a computer to carry out operations without having been expressly programmed to do so. In this situation, learning is about identifying complex shapes and making intelligent decisions. The challenge in completing this task, given all the available inputs, is that the set of potential decisions is typically quite difficult to enumerate. Machine learning algorithms have been developed with the goal of learning about the problem to be handled based on a collection of limited data from this problem in order to get around this challenge. This textbook presents the scientific foundations of supervised learning theory, the most widespread algorithms developed according to this framework, as well as the semi-supervised and the learning-to-rank frameworks, at a level accessible to master's students. The aim of the book is to provide a coherent presentation linking the theory to the algorithms developed in this field. In addition, this study is not limited to the presentation of these foundations, but it also presents exercises, and is intended for readers who seek to understand the functioning of these models sometimes designated as black boxes.
410  0$aCognitive Technologies,$x2197-6635
606   $aArtificial intelligence
606   $aMachine learning
606   $aInformation storage and retrieval systems
606   $aComputer vision
606   $aPython (Computer program language)
606   $aStatistics
606   $aArtificial Intelligence
606   $aMachine Learning
606   $aInformation Storage and Retrieval
606   $aComputer Vision
606   $aPython
606   $aBayesian Inference
615  0$aArtificial intelligence.
615  0$aMachine learning.
615  0$aInformation storage and retrieval systems.
615  0$aComputer vision.
615  0$aPython (Computer program language)
615  0$aStatistics.
615 14$aArtificial Intelligence.
615 24$aMachine Learning.
615 24$aInformation Storage and Retrieval.
615 24$aComputer Vision.
615 24$aPython.
615 24$aBayesian Inference.
676   $a006.3
700   $aAmini$b Massih-Reza$4aut$4http://id.loc.gov/vocabulary/relators/aut$01060956
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9911034943203321
996   $aAdvanced Supervised and Semi-supervised Learning$94449169
997   $aUNINA