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19 Jan 2012: and. J(m)xδ =. g. xtδ. µ(m)xe ,µl,µn. (24). Thus the model parameters are compensated by. ... Forexample, using the initial noise estimate, RVTSJ performancevaried from 27.5% to 31.7%, while the performance of MLestimated noise only varied from 24.3%

19 Jan 2012: 23)), finds the most violated constraint (Eq. (24)), andadds it to the working set. ... Parallelingthe loop for Eq. (24) will lead to a substantial speed-up in thenumber of threads.

19 Jan 2012: SpeechLang., vol. 24, no. 4, pp. 648–662, 2010. [6] B. Taskar, “Learning structured prediction models: a large marginapproach,” Ph.D.

19 Oct 2012: α̂(r) = argminα. {. F(. α, w(r), O(r); α̂(r1))}. (15). Conditional Maximum Likelihood[24]:. ... . . . . . . . vj V (24). whereV is the vocabulary of segment identities.

8 May 2012: 24 Engineering Part IB: Paper 8 Image Matching. Interest Point Detection - Blobs.

19 Oct 2012: Flat Direct Model [11] SCRF [23] or CAug [13]. M3N [18] orLarge Margin (Multi-Class) SVM [24]. ... Reformulate equation (24) in the form of (17). 1. 2||ᾱ||22+. C.

10 Feb 2012: The like-lihood is then computed as. p(yyyt |m). p(xxx|yyyt )p(xxx|m)dxxx (24). ... Though intuitively well motivated, from (24) it can be seen that the likelihood is notmathematically consistent.

29 May 2012: All transitions in this language. 15. 4. classifiers. ml mpeDiscriminative training L2 std dev subset all dataFixed scaling factor 30.5 24.4Scaling factor 53.5. ... Full language model. 2.0 1006 24.88.5 1006 24.41.2 1005 24.42.7 1005 24.54.4 1005 30.8.

22 Dec 2012: al. [24]. In this work, we follow these injointly modelling appearance and semantic labels. ... Graph., vol. Vol. 24, pp. pp. 595–600, 2005. [5] C. Rother, V.