Pattern recognition and machine learning
I tiakina i:
| Kaituhi matua: | |
|---|---|
| Hōputu: | Pukapuka |
| Reo: | Ingarihi |
| I whakaputaina: |
New York, USA
Springer Science and Business Media
2006
|
| Rangatū: | Information Science and Statistics
|
| Ngā marau: | |
| Urunga tuihono: | Disponible Online |
| Ngā Tūtohu: |
Tāpirihia he Tūtohu
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
Rārangi ihirangi:
- 1 Introduction 2 Probability Distributions 3 Linear Models for Regression 4 Linear Models for Classification 5 Neural Networks 6 Kernel Methods 7 Sparse Kernel Machines 8 Graphical Models 9 Mixture Models and EM 10 Approximate Inference 11 Sampling Methods 12 Continuous Latent Variables 13 Sequential Data 14 Combining Models Appendix A Data Sets Appendix B Probability Distributions Appendix C Properties of Matrices Appendix D Calculus of Variations Appendix E Lagrange Multipliers References Index