000			02842ntdaa2200313 ab4500
003			UnInEc
005			20240401124147.0
006			a||||g ||i| 00| 0
008			140501s9999 mx ||||f |||| 00| 0 spa d
020			_a9780262039406
040			_aCIBESPAM MFL
041			_aeng
082			_a006.3 _bM699
100			_aMohri, Mehryar
245			_aFoundations of Machine Learning.
250			_aSecond Edition
260			_aCambridge _bThe MIT Press _c2018
300			_axv, 486 pages; _bFigures;
505			_a-1 Introduction -2 The PAC Learning Framework -3 Rademacher Complexity and VC-Dimension -4 Model Selection -5 Support Vector Machines -6 Kernel Methods -7 Boosting -8 On-Line Learning -9 Multi-Class Classification -10 Ranking -11 Regression -12 Maximum Entropy Models -13 Conditional Maximum Entropy Models -14 Algorithmic Stability -15 Dimensionality Reduction -16 Learning Automata and Languages -17 Reinforcement Learning -Conclusion
520			_aThis book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
650			_aMachine Learning
650			_aComputing
650			_aAutómata
700			_aRostamizadeh, Afshin
700			_aTalwalkar, Ameet
912			_c2024-01-04 _dPaúl Villacreses
913			_aTIC _bCC _dSCSAS
942			_2ddc _cBK
999			_c13032 _d13032