Artificial neural networks provide a simple framework for describing learning from examples. This coherent account of important concepts and techniques of statistical mechanics and their application to learning theory comes with background material in mathematics and physics, plus many examples and exercises, making it ideal for courses, self-teaching, or reference.
Artificial neural networks provide a simple framework for describing learning from examples. This coherent account of important concepts and techniques of statistical mechanics and their application to learning theory comes with background material in mathematics and physics, plus many examples and exercises, making it ideal for courses, self-teaching, or reference.
1. Getting started 2. Perceptron learning - basics 3. A choice of learning rules 4. Augmented statistical mechanics formulation 5. Noisy teachers 6. The storage problem 7. Discontinuous learning 8. Unsupervised learning 9. On-line learning 10. Making contact with statistics 11. A bird's eye view: multifractals 12. Multilayer networks 13. On-line learning in multilayer networks 14. What else? Appendix A. Basic mathematics Appendix B. The Gardner analysis Appendix C. Convergence of the perceptron rule Appendix D. Stability of the replica symmetric saddle point Appendix E. 1-step replica symmetry breaking Appendix F. The cavity approach Appendix G. The VC-theorem.
1. Getting started 2. Perceptron learning - basics 3. A choice of learning rules 4. Augmented statistical mechanics formulation 5. Noisy teachers 6. The storage problem 7. Discontinuous learning 8. Unsupervised learning 9. On-line learning 10. Making contact with statistics 11. A bird's eye view: multifractals 12. Multilayer networks 13. On-line learning in multilayer networks 14. What else? Appendix A. Basic mathematics Appendix B. The Gardner analysis Appendix C. Convergence of the perceptron rule Appendix D. Stability of the replica symmetric saddle point Appendix E. 1-step replica symmetry breaking Appendix F. The cavity approach Appendix G. The VC-theorem.
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
USt-IdNr: DE450055826
