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21 - 40 of 261 search results for katalk:za31 24 / / / / / |u:mlg.eng.cam.ac.uk where 0 match all words and 261 match some words.

  1. Results that match 1 of 2 words

  2. 3F3: Signal and Pattern Processing Lecture 1: Introduction to ...

    https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect1.pdf
    19 Nov 2023: Dataset Data dim. Sample size MLE Regression Corr. dim.Swiss roll 3 1000 2.1(0.02) 1.8(0.03) 2.0(0.24)Faces 64 64 698 4.3

  3. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect01.pdf
    19 Nov 2023: Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning 24 / 26.

  4. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect07.pdf
    19 Nov 2023: But we can not usually simulate Hamiltonian dynamics exactly. Ghahramani & Rasmussen (CUED) Lecture 7 and 8: Markov Chain Monte Carlo 24 / 28.

  5. - Machine Learning 4F13, Michaelmas 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect0304.pdf
    19 Nov 2023: 1 0 1 22. 1. 0. 1. 2M=0. 1 0 1 24. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1.

  6. chuesann.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/ChuGhaWil04b.pdf
    13 Feb 2023: H70.13% 70.77% 72.28% 71.70%. QobsE. 46.51% 24.69% 46.86% 23.96%Qobs. C73.29% 70.52% 72.64% 70.86%.

  7. - Machine Learning 4F13, Spring 2014

    https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect0304.pdf
    19 Nov 2023: 1. 0. 1. 2M=0. 1 0 1 24. 2. 0. 2. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1.

  8. - Machine Learning 4F13, Spring 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect0304.pdf
    19 Nov 2023: 1. 0. 1. 2M=0. 1 0 1 24. 2. 0. 2. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1.

  9. paper.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/KimGha08.pdf
    13 Feb 2023: GPC log-ev -24.40.6 -41.80.8 -51.81.1 -60.21.0error(%) 3.700.36 4.900.77 7.500.88 8.151.42. robust log-ev ... MS-robust- log-ev -24.40.6 -41.00.7 -50.50.7 -58.60.8GPC error(%) 3.700.36 4.540.62 6.760.76 6.850.73.

  10. book

    https://mlg.eng.cam.ac.uk/pub/pdf/PerGhaPon07.pdf
    13 Feb 2023: 1.23). subject to:. wt φt(xn, ynt) wt φt(xn, yt) Mynt,yt ξnt n, t, yt (1.24). ... We end up having a significant reduction in the number ofconstraints2 in our optimisation formulation for CGMs in (1.23)-(1.24).

  11. Bayesian Hierarchical Clustering Katherine A. Heller…

    https://mlg.eng.cam.ac.uk/zoubin/papers/icml05heller.pdf
    27 Jan 2023: 1 2 3 4 5 6 8 9 10 7 11 12 13 14 15 16 18 20 19 17 21 22 23 24 25 26 270. ... 0.2231.24. 3.6. 59.9. 0 1 2 3 4 5 6 74.

  12. The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...

    https://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf
    27 Jan 2023: The Infinite Hidden Markov Model. Matthew J. Beal Zoubin Ghahramani Carl Edward Rasmussen. Gatsby Computational Neuroscience UnitUniversity College London. 17 Queen Square, London WC1N 3AR, Englandhttp://www.gatsby.ucl.ac.uk. {m.beal,zoubin,edward

  13. Sequential Decisions

    https://mlg.eng.cam.ac.uk/zoubin/SALD/week13sequential.pdf
    27 Jan 2023: solutions – the latter relating to “improper” priors! 24. Appendix: Background on the Von Neumann - Morgenstern theory of cardinal.

  14. Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning

    https://mlg.eng.cam.ac.uk/pub/pdf/ZhuKanGha04a.pdf
    13 Feb 2023: 50.27 (86) 0.24 (92) 0.15 0.18 0.40 (85) 0.02 0.12 0.09. ... 0.27 (26) 0.13 (25) 0.03 0.11 0.31 (24) -0.89 -0.80 -0.65100 64.6 2.1 59.0 3.6 58.5 2.9

  15. A Brief Overview of Nonparametric Bayesian Models NIPS 2009 ...

    https://mlg.eng.cam.ac.uk/zoubin/talks/nips09npb.pdf
    27 Jan 2023: 21). Given s, the distribution of Z becomes:. p( Z | x , s, µ ( 1 : ) ) p( Z | x , µ ( 1 : ) ) 1µ I (0 s µ ) (24).

  16. Cambridge Machine Learning Group Publications

    https://mlg.eng.cam.ac.uk/pub/authors/
    13 Feb 2023: Publications, Machine Learning Group, Department of Engineering, Cambridge. current group:. [former members:. [by year:. [Tameem Adel. George Nicholson, Marta Blangiardo, Mark Briers, Peter J Diggle, Tor Erlend Fjelde, Hong Ge, Robert J B Goudie,

  17. Bayesian Classifier Combination Hyun-Chul Kim Zoubin GhahramaniKorea…

    https://mlg.eng.cam.ac.uk/pub/pdf/KimGha12.pdf
    13 Feb 2023: Bagging predictors. Ma-chine Learning 24, 123–140. Breiman, L., October 2001. Random forests. ... Vol. 24. pp. 791–798. Takács, G., Pilászy, I., Németh, B., Tikk, D., 2007.

  18. G:\bioinformatics\ISMB\btq210.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/SavGhaGrietal10.pdf
    13 Feb 2023: 5634 2 2.6108 22/23 15/84 Nucleus32991 2 7.9105 18/23 24/84 Macromolecular complex. ... BMC Bioinformatics, 8,283. i165. by on July 24, 2010 http://bioinform. atics.oxfordjournals.orgDownloaded from.

  19. Function factorization using warped Gaussian processes Mikkel N.…

    https://mlg.eng.cam.ac.uk/pub/pdf/Sch09.pdf
    13 Feb 2023: 1. 2 1. 2erf( g. 2. ). ). , (24). parameterized by log scale parameters θh = {λi}.This warp function was suggested by Schmidt and Lau-rberg (2008) and has the property that it

  20. Learning with Multiple Labels

    https://mlg.eng.cam.ac.uk/pub/pdf/JinGha02a.pdf
    13 Feb 2023: Class Name ecoli wine pendigit iris glass. 1 extra label Naive 17.3% 10% 14.2% 18.5% 24.9% by random.

  21. doi:10.1016/j.patrec.2005.09.027

    https://mlg.eng.cam.ac.uk/pub/pdf/KimKimGha06a.pdf
    13 Feb 2023: Martinez, A., Benavente, R., 1998. The ar face database. CVC TechnicalReport #24. ... Pattern Anal. Mach. Intell. 24 (5), 707–711. Neal, R., 1997. Monte Carlo implementation of Gaussian process modelsfor Bayesian regression and classification.

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