A first course in machine learning

A First Course in Machine Learning

Volumn , Issue , 2011, Pages 1-284

  Rogers, Simon ^a   Girolami, Mark ^b  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85059570920     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (46)

1. F. Galton. Regression towards mediocrity in hereditary stature. Anthopological Miscellanea, 15:246-263, 1886.
2. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning:Data Mining, Inference, and Prediction. Springer, second edition, 2009.
3. K. B. Petersen and M. S. Pedersen. The matrix cookbook. http://www2.imm.dtu.dk/pubdb/p.php?3274, October 2008.
4. Christopher Bishop. Pattern Recognition and Machine Learning. Springer, 2007.
5. J.H. McColl. Probability. Elsevier, 1995.
6. Paul Meyer. Introductory Probability and Statistical Applications. Addison-Wesley, 1978.
7. J. Rosenthal. A First Look at Rigorous Probability Theory. World Scientific Publishing Company, 2006.
8. Michael Tipping and Christopher Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 61(3):611-622, 1999.
9. Ben Calderhead and Mark Girolami. Estimating Bayes factors via thermodynamic integration and population mcmc. Comput. Stat. Data Anal., 53:4028-4045, October 2009.
10. Andrew Gelman, John B. Carlin, Hal S. Stern, and Donald B. Rubin. Bayesian Data Analysis. Chapman & Hall/CRC, second edition, 2004.
11. Michael Isard and Andrew Blake. Contour tracking by stochastic propagation of conditional density. In European Conference on Computer Vision, pages 343-356, Springer, 1996.
12. Michael Jordan, editor. Learning in Graphical Models. MIT Press, 1999.
13. Christian Robert. The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation. Springer, second edition, 2007.
14. Tian-Rui Xu et al. Inferring signaling pathway topologies from mulitple perturbation measurement of specific biochemical species. Science Signalling, 3(113), 2010.
15. Christophe Andrieu, Nando de Freitas, Arnaud Doucet, and Michael Jordan. An introduction to mcmc for machine learning. Machine Learning, 50:5-43, 2003.
16. Siddhartha Chib. Understanding the Metropolis-Hasting algorithm. The American Statistican, 49(4):327-335, 1995.
17. Arnaud Doucet, Nando de Freitas, and Neil Godron, editors. Sequential Monte Carlo Methods in Practice. Springer, 2010.
18. Andrew Gelman, John B. Carlin, Hal S. Stern, and Donald B. Rubin. Bayesian Data Analysis. Chapman and Hall/CRC, second edition, 2004.
19. W.R. Gilks, S. Richardson, and D. Spiegelhalter, editors. Markov Chain Monte-Carlo in Practice. Chapman and Hall/CRC, 2005.
20. Mark Girolami and Ben Calderhead. Riemann manifold Langevin and Hamiltonian Monte Carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73(2):123-214, 2011.
21. Jun Liu. Monte Carlo Strategies in Scientific Computing. Springer, 2008.
22. Carl Rasmussen and Christopher Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006.
23. Simon Rogers, Richard Scheltema, Mark Girolami, and Rainer Breitling. Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinformatics, 25(4):512-518, 2009.
24. Michael Tipping and Alex Smola. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211-244, 2001.
25. Christopher Williams and David Barber. Bayesian classification with Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12):1342-1351, 1998.
26. Ken Binmore and Joan Davies. Calculus: Concepts and Methods. Cambridge University Press, 2002.
27. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
28. Richard Duda, Peter Hart, and David Stork. Pattern Classification. Wiley-Interscience, second edition, 2000.
29. T. Furey et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16(10):906-914, 2000.
30. Brian Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, 1996.
31. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
32. David Blei, Andrew Ng, and Michael Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
33. Igor Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, and Steven White. Model-based clustering and visualization of navigation patterns on a web site. Data Mining and Knowledge Discovery, pages 399-424, 2003.
34. Guojun Gan, Chaogun Ma, and Jianhong Wu. Data Clustering: Theory, Algorithms, and Applications. Society for Industrial Mathematics, 2007.
35. Anil K. Jain. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31:651-666, 2010.
36. Anil K. Jain and R.C. Dubes. Algorithms For Clustering Data. Prentice Hall, 1988. A clustering textbook that is now out of print but available free from the authors’ website: http://www.cse.msu.edu/~jain/Clustering_ Jain_Dubes.pdf
37. Anil K. Jain, M.N. Murty, and P.J. Flynn. Data clustering: A review. ACM Computing Reviews, pages 264-323, 1999.
38. G. McLachlan and D. Peel. Finite Mixture Models. Wiley, 2000.
39. Carl Rasmussen. The infinite Gaussian mixture model. In In Advances in Neural Information Processing Systems 12, pages 554-560, 2000.
40. Christopher M. Bishop. Variational principal components. In Proceedings Ninth International Conference on Artificial Neural Networks, ICANN’99, pages 509-514, 1999.
41. I.T. Jolliffe. Principal Component Analysis. Springer, second edition, 2002.
42. Michael Jordan, Z. Ghahramani, T.S. Jaakkola, and L.K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37:183-233, 1999.
43. Arto Klami and Samuel Kaski. Probabilistic approaches to detecting dependencies between data sets. Neurocomputing, 72:39-46, 2008.
44. S. Roweis and Z. Ghahramani. A unifying review of linear Gaussian models. Neural Computation, 11(2):305-345, 1999.
45. Michael Tipping. Probabilistic visualisation of high-dimensional binary data. In Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems II, pages 592-598, Cambridge, MA, USA, 1999. MIT Press.
46. Michael Tipping and Christopher Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 61(3):611-622, 1999.