Search

22 Sep 2015: en Tue, 22 Sep 2015 07:38:20 GMT https://getnikola.com/ http://blogs.law.harvard.edu/tech/rss EPSRC Funded Research Project

10 Nov 2015: 0.1 0.2 0.5 1 2 5 10 20 40. 0.1. 0.2. ... 0.5. 1. 2. 5. 10. 20. 40. False Alarm probability (in %). Miss. pro. babi. lity (i. n %. ). GMM SVM. MAP (τ)EER.

10 Nov 2015: 20. -10. 0. 10. 20. -10. 0. 10. 0. 0.01. 0.02. ... 10. 010. 20. -10. 0. 10. 20. 0. 0.005. 0.01. P.

10 Nov 2015: Bayes’ Rule is sometimes remembered as. posterior likelihood prior. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing. ... 10. 20. 30. 40. 50. 60. 70. 80. 90. 100. False Positive.

10 Nov 2015: Bayes’ Rule is sometimes remembered as. posterior likelihood prior. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing. ... 10. 20. 30. 40. 50. 60. 70. 80. 90. 100. False Positive.

10 Nov 2015: 0.05. 0.1. 0.15. 0.2. 0.25. 0.3. 0.35. 20. 4 Engineering Part IIB: Module 4F10 Statistical Pattern Processing. ... 4. 2. 0. 2. 4. 6. 83 ITERATIONS. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing.

10 Nov 2015: 05. 1015. 2025. 0. 5. 10. 15. 20. 250. 0.05. 0.1.

3 Jun 2015: 20, no. 1, pp. 14–22, 2012. [17] G. Hinton, L. Deng, D. ... 34, no. 1, pp. 25–40, 2001. [20] H. Misra, H. Bourlard, and V.

10 Nov 2015: 1. 1. x1. 1x2. 0.5NOR. (d) NOR operator. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing.

18 May 2015: Though for speaker adaptation this may be reason-able [20], for language adaptation this is expected to be suboptimalas different eigen-languages may have different acoustic realisationsof context-dependencies. ... Rep. CUED/F-INFENG/TR263,Cambridge

25 Nov 2015: Classifier 1. Processing. 20. 4F10: Statistical Pattern Processing. Adaptive Directed Acyclic Graph.

6 Apr 2015: Though for speaker adaptation this may be reason-able [20], for language adaptation this is expected to be suboptimalas different eigen-languages may have different acoustic realisationsof context-dependencies. ... Rep. CUED/F-INFENG/TR263,Cambridge

21 Jan 2015: This was the approach adopted from the graphemicModern Standard Arabic (MSA) systems in [19] and the system forthe RATS data in [20]. ... 26, no. 4, pp. 229–243, 2012. [20] Mark J. F. Gales and Federico Flego, “Model-based ap-proaches for degraded

3 Jun 2015: The training data for the other languageswas retrieved from the Leipzig corpora [20]. ... 485–525. [19] B. Carpenter. Lingpipe dynamiclmclassifier. [Online]. Avail-able: http://alias-i.com/lingpipe/docs/api/com/aliasi/classify/DynamicLMClassifier.html.

10 Nov 2015: 1. 1. 1. 1. 1.5 AND. 25. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing.

9 Oct 2015: Joint decoding can be viewed as a single multi-stream sys-tem [20, 21]. ... 20, no. 1, pp. 14–22, 2012. [18] G. Hinton, L. Deng, D.

3 Feb 2015: 5. 330. mean surface density (mg/cm )20 660. (a) Male mean CMSDp (vertex)p (cluster)% change per s.d. ... exploration 1 Hgt Age Wgt Shp Site Hgt 2.20 102 40 3.50n=308 1 S0 Age Wgt Shp Site S0 5.09 107 208 7.84males 1 S0 Age

18 May 2015: 20]. This expert derived set of decision tree ques-tions was compared with the unicode-based graphemic approach de-scribed in this paper. ... 26, no. 4, pp. 229–243, 2012. [20] M.J.F. Gales and F.

16 Jun 2015: Theimplementation of the incremental algorithm uses the author’sFlipsta C++ library for finite-state automata [20], open-sourcedunder the permissive Apache License [21], which allows morelazy operations. ... 101. 102. 103. Mem. ory. use. (MB. ). 0 10

9 Oct 2015: The training data for the other languageswas retrieved from the Leipzig corpora [20]. ... 485–525. [19] B. Carpenter. Lingpipe dynamiclmclassifier. [Online]. Avail-able: http://alias-i.com/lingpipe/docs/api/com/aliasi/classify/DynamicLMClassifier.html.

10 Nov 2015: 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20.8. ... 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing. Variational Approaches.

20 Nov 2015: x =. [. x1x2. ]. Φ(x) =. 12x12x22x1x2x21x22. 32. 20 Engineering Part IIB: 4F10 Statistical Pattern Processing.

26 Jan 2015: Predictive value of BMD for hip and other fractures. Journal of Bone and Mineral Research 20 (7),1185–1194. ... Medical Image Analysis 20, 249–264. Treece, G. M., Gee, A. H., Mayhew, P.

10 Nov 2015: 20 Engineering Part IIB: 4F10 Statistical Pattern Processing. Determining the Bias.

10 Nov 2015: 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20.3. ... 4F10: Statistical Pattern Processing. EM Example. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20.3.

11 Feb 2015: The speech recogniser with an unbiased language model obtainsa word error rate of 40.4 %. 1http://www.wiki.cl.cam.ac.uk/rowiki/NaturalLanguage/ALTA. 20. 25. 30. 35. Wor. der. ... Constrained ASR. Longest. Oracle. (a) Picking one whole transcription. 20.

10 Sep 2015: 20. [37] A. Klin, W. Jones, R. Schultz, F. Volkmar, The enactive mind, or from actions to563cognition: lessons from autism, Philos. ... Audio Speech Lang. Process. 20 (5).591. [47] J. Latorre, V. Wan, M.

10 Sep 2015: 20. [37] A. Klin, W. Jones, R. Schultz, F. Volkmar, The enactive mind, or from actions to563cognition: lessons from autism, Philos. ... Audio Speech Lang. Process. 20 (5).591. [47] J. Latorre, V. Wan, M.

17 Apr 2015: is function is applied separately to each feature in (20). 4.1 Instance: likelihood score-spaces. ... f(o2) =. 0.4 0.1 00 0.2 0.20 0 0. . (22a)e way to read this matrix is.

1 Jun 2015: 194 Bayesian log-linear models. 20. 4.1 Bayesian models. 204.2 Bayesian conditional models. ... f(o2) =. 0.4 0.1 00 0.2 0.20 0 0. . (24a)e way to read this matrix is.

27 Oct 2015: words in the Resource Management (RM) task (1988-1992)[195] to 20,000 words in the Wall.

6 Feb 2015: CHAPTER 2. STATISTICAL MACHINE TRANSLATION 20. Och et al. (1999) notice that using an erroneous version of the source-channel model, that is using the following equation:.

2 Feb 2015: 2.20). where f̃ is a K-dimensional vector. The functions F and len are sufficient statistics for.