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13 Feb 2023: Next anyparameters of the density model can be integrated outto produce a predictive clustering of unseen test data. ... lected wheat types) and leave 5 data points from eachwheat type as unobserved test points.

27 Jan 2023: We propose estimating the likelihood of a test sequence given a learned model using particlefiltering. ... 6Different particle initialisations apply if we do not assume that the test sequence immediatelyfollows the training sequence.

13 Feb 2023: the in-ferred test latent variable z with respect to the train-ing latent variables Z. ... boldface is significant using a one-sided paired t-test with 95% confidence.

13 Feb 2023: c) (d)Figure 4: Olivetti faces a) Test images on the left, withreconstructed bottom halves on the right. ... Fig 4ashows six bottom-half test set reconstructions on theright, compared to the ground truth on the left.

13 Feb 2023: In almost all of our tests, Archipelagohad lower test classification error than the NCNM. ... Itimproves over mixture-based Bayesian approaches toSSL while still modeling complex density functions.In empirical tests, our model compares favorably

27 Jan 2023: Uniform U(0, θ] distribution, θ > 0. We select at random m n disks, having a common θ for failure We select n of these (at random) and test them until

13 Feb 2023: 0.23370.0072. Table 2: Test results of the three algorithms using a Gaussian kernel. ... Figure 4 presents the test results of the three algorithms for different numbers ofselected genes.

13 Feb 2023: The data was randomly split into atraining set of 130 countries, and a test set of 14 coun-tries. ... For each test country, one randomly selectedindicator was observed, and the remainder held outfor prediction.

13 Feb 2023: Althoughdeposit failure feedback was not available to the learner, thedeposit success is good across 10 test trials and four differenttraining setups. ... Second, Tab. III reports the block-deposit success rates for10 test runs (and four different

27 Jan 2023: Some Comparisons. Table 1: Test errors and predictive accuracy (smaller is better) for the GP classifier, the supportvector machine, the informative vector machine, and the sparse pseudo-inputGP classifier. ... Data set GPC SVM IVM SPGPC. name train:test