Transcription of INTRODUCTION MACHINE LEARNING
{{id}} {{{paragraph}}}
Example: quiz answers
INTRODUCTION MACHINE LEARNING
INTRODUCTIONTOMACHINE LEARNINGAN EARLY DRAFT OF A PROPOSEDTEXTBOOKNils J. NilssonRobotics LaboratoryDepartment of Computer ScienceStanford UniversityStanford, CA 94305e-mail: 3, 1998 Copyrightc 2005 Nils J. NilssonThis material may not be copied, reproduced, or distributed without thewritten permission of the copyright INTRODUCTION .. is MACHINE LEARNING ? .. of MACHINE LEARNING .. of MACHINE LEARNING .. LEARNING Input-Output Functions .. of LEARNING .. Vectors .. Regimes .. Evaluation .. LEARNING Requires Bias .. Sample Applications .. Sources .. Bibliographical and Historical Remarks .. 132 Boolean Representation .. Algebra .. Representations .. Classes of Boolean Functions .. and Clauses .. Functions .. Functions .. Lists .. and Voting Functions .. Separable Functions .. Summary.
and psychologists study learning in animals and humans. In this book we fo-cus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational models.
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: