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Split and Merge EM Algorithm for Improving Gaussian Mixture Density…
https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha00b.pdf13 Feb 2023: toimprove the likelihood of both the training data and of held-out test data. ... 2.In Fig. 2, the upper (lower) trajectory corresponds tothe training (test) data. -
FAST ONLINE ANOMALY DETECTION USING SCAN STATISTICS Ryan Turner ...
https://mlg.eng.cam.ac.uk/pub/pdf/TurBotGha10.pdf13 Feb 2023: The compu-tational burden is small since the routine only needs to berun when configuring the test. ... We compareit to the CUSUM method, linear trend methods, and uni-formity tests. -
WolGha05 handout
https://mlg.eng.cam.ac.uk/zoubin/papers/WolGha06.pdf27 Jan 2023: Now, imagine we get new information in the form of a positive blood test. ... Let us denote by B, the event that the blood test is positive. -
workshop_abstract.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/WilGha08.pdf13 Feb 2023: This experiment was designed to test the average predictiveperformance of the algorithms. ... The results are shown in table 1. Secondly, we designed an experiment to test the performance of the algorithm on new movieswith no, or few, reviews. -
Learning Depth From Stereo Fabian H. Sınz1, Joaquin Quiñonero ...
https://mlg.eng.cam.ac.uk/pub/pdf/SinQuiBaketal04.pdf13 Feb 2023: The remaining 792 were used as test set. Classical calibration. During bundle adjustment, several camera parameterswere highly correlated with others. ... Fig. 5 shows the position error according to the test points actualdepth and according to the image -
Bayesian HC research talk
https://mlg.eng.cam.ac.uk/zoubin/p8-07/lect4s.ppt27 Jan 2023: Unlabelled Test Images: 22,000 images. For each training and test image we can store a vector of 240 binary color and texture features. ... about 0.2 sec on this laptop to query 22,000 test images. -
Sparse Gaussian Processes using Pseudo-inputs Edward Snelson Zoubin…
https://mlg.eng.cam.ac.uk/zoubin/papers/nips05spgp.pdf27 Jan 2023: Once the inversion is done, prediction isO(N ) for thepredictive mean andO(N 2) for the predictive variance per new test case. ... We have demonstrated a significant decrease in test error over the other methods for a givensmall pseudo/active set size. -
System Identification inGaussian Process Dynamical Systems Ryan…
https://mlg.eng.cam.ac.uk/pub/pdf/TurDeiRas09.pdf13 Feb 2023: of test data; we trainedon daily snowfall from Jan. ... We do not report results for GPDM on the real data since it was too slow to run on the large test set. -
AA06.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/GirRasQuiMur03.pdf13 Feb 2023: the density of the actual true test output under the Gaussianpredictive distribution and use its negative log as a measure of loss. ... The training and test data consist of pH values (outputsy of the process) anda control input signal (u). -
Large Scale Nonparametric Bayesian Inference:Data Parallelisation in…
https://mlg.eng.cam.ac.uk/pub/pdf/DosKnoMohGha09.pdf13 Feb 2023: Initially, a largenumber of features are added, which provides improvements in the test likelihood. ... Table 2 summarises the data and shows thatall approaches had similar test-likelihood performance.
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