"Probably approximately correct: nature's algorithms for learning and prospering in a complex world"

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Book	Author(s)	Year	Publisher
Probably approximately correct: nature's algorithms for learning and prospering in a complex world	Valiant, Leslie	2014	Basic Books
Probably approximately correct: nature's algorithms for learning and prospering in a complex world /	Valiant, Leslie.	c2013.	Basic Books,
Inductive Learning Algorithms for Complex Systems Modeling	Hema R. Madala; Alexy G. Ivakhnenko	1994	Routledge
Engaging Minds: Learning and Teaching in A Complex World	Brent Davis; Dennis J. Sumara; Rebecca Luce-Kapler	May 1, 2000	Lawrence Erlbaum
Engaging minds: learning and teaching in a complex world /	Davis, Brent.; Sumara, Dennis J.,; Luce-Kapler, Rebecca.	2000.	L. Erlbaum Associates,
Learning causality in a complex world: understandings of consequence	Tina Grotzer	2012	Rowman & Littlefield Education
Algorithms for Reinforcement Learning	Csaba Szepesvári		Springer International Publishing
Algorithms for reinforcement learning	Csaba Szepesvári	2010	Morgan & Claypool
Nature's temples: the complex world of old-growth forests /	Maloof, Joan,; Joslin, Andrew,		Timber Press
Complex fluids: Modeling and Algorithms	Pierre Saramito	1st ed. 2016	Springer International Publishing
A Framework for Learning: For Adults with Profound and Complex Learning Difficulties	Caroline Allen	2001	Routledge
Genetic Algorithms for Machine Learning	John J. Grefenstette	1994	Springer US
HRD in a Complex World	Monica Lee	2003	Routledge
Simple Rules for a Complex World	Richard Allen Epstein	1995	Harvard University Press
Simple rules for a complex world /	Epstein, Richard Allen,	1995.	Harvard University Press,
Social Semiotics for a Complex World	Bob Hodge	2017	Polity
Approximately /	Gregorio, Guillermo E.,; Pakula, Eric,; Maneri, Mat,; Karayorgis, Pandelis,; Lockwood, John
Learning and complex behavior /	Donahoe, John W.,; Palmer, David C.,; Dorsel, Vivian Packard.	[1994], ©1994.	Allyn and Bacon,
Learning and complex behavior	John W. Donahoe	2004	Ledgetop Pub.
Algorithms in Machine Learning Paradigms	Jyotsna Kumar Mandal; Somnath Mukhopadhyay; Paramartha Dutta; Kousik Dasgupta	1st ed. 2020	Springer Nature Singapore
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Probably Approximately Correct Learning		Encyclopedia of Machine Learning and Data Mining	Claude Sammut; Geoffrey I. Webb	2nd ed. 2017	Springer US
Probably Approximately Correct Learning		Encyclopedia of Computer Science and Technology: Volume 31 - Supplement 16: Artistic Computer Graphics to Strategic Information Systems Planning	Allen Kent; James G. Williams	1994	Routledge
Probably Approximately Correct Learning		Encyclopedia of Machine Learning	Claude Sammut; Geoffrey I. Webb	2010	Springer US
Refutably probably approximately correct learning	Satoshi Matsumoto; Ayumi Shinohara	Algorithmic Learning Theory: 4th International Workshop on Analogical and Inductive Inference, AII ' 94, 5th International Workshop on Algorithmic Learning Theory, ALT ' 94, Reinhardsbrunn Castle, Germany, October 10 - 15, 1994. Proceedings	Setsuo Arikawa; Klaus P. Jantke	1994	Springer Berlin Heidelberg
1722 Probably approximately correct learning		Advances in learning theory: methods, models, and applications	Johan A. K. Suykens	2003	IOS Press
Probably Approximately Correct		7 Algorithm Design Paradigms	Sung-Hyuk Cha
The Probably Approximately Correct Learning Model		An Introduction to Computational Learning Theory	Michael J. Kearns; Umesh Vazirani	1994	MIT Press
Probably Approximately Correct Theory		Encyclopedia of the Sciences of Learning	Norbert M. Seel	2012	Springer US
Probably approximately correct software		Thoughtful machine learning with Python: a test-driven approach	Kirk, Matthew	2017	O'Reilly Media, Inc.
1: The Probably Approximately Correct Learning Model		An Introduction to Computational Learning Theory	Michael J. Kearns; Umesh Vazirani	1994	MIT Press,
Learning Theory: the Probably Approximately Correct Framework	Mathukumalli Vidyasagar	Encyclopedia of Systems and Control	John Baillieul; Tariq Samad	2nd ed. 2021	Springer International Publishing
1. The Probably Approximately Correct Learning Model		An introduction to computational learning theory /	Kearns, Michael J.; Vazirani, Umesh Virkumar.	[1994], ©1994.	MIT Press,
Probably Approximately Correct Interpolants Generation	Bai Xue; Naijun Zhan	Dependable Software Engineering. Theories, Tools, and Applications: 6th International Symposium, SETTA 2020, Guangzhou, China, November 24–27, 2020, Proceedings	Jun Pang; Lijun Zhang	1st ed. 2020	Springer International Publishing
174 Probably, approximately correct (PAC) learning: A statistical approach		Mathematics and computation	Wigderson A	2019	Princeton University Press
The Probably Approximately Correct (PAC) and Other Learning Models	David Haussler; Manfred Warmuth	Foundations of Knowledge Acquisition: Machine Learning	Alan L. Meyrowitz; Susan Chipman	1993	Springer US
The Probably Approximately Correct (PAC) and Other Learning Models	David Haussler; Manfred Warmuth	The Mathematics of Generalization: The Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning	David H. Wolpert	1995	Routledge
Chapter 1 Probably Approximately Correct Software		Thoughtful Machine Learning With Python	matthew kirk	2017-01-27	Oriely
Chapter 1 Probably Approximately Correct Software		Thoughtful machine learning with Python: a test-driven approach	Kirk, Matthew	2017	O'Reilly Media
Performance Estimation and Probably Approximately Correct Computation	Cesare Alippi	Intelligence for Embedded Systems: A Methodological Approach	Cesare Alippi	2014	Springer International Publishing
Probably Approximately Correct Learning and Decision-Theoretic Generalizations: David Haussler	Paul Smolensky; Michael C. Mozer; David E. Rumelhart	Mathematical Perspectives on Neural Networks	Paul Smolensky; Michael C. Mozer; David E. Rumelhart	1996	Routledge
Chapter	Author(s)	Book	Book Authors	Year	Publisher

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