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13 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.

19 Nov 2023: Constraint-Based Learning: Use statistical tests of marginal and conditionalindependence. Find the set of DAGs whose d-separation relations match theresults of conditional independence tests.

13 Feb 2023: Figure 1 shows the training points, test points, decision boundary from GPCand decision boundary from robust GPC. ... We created 10 pairs of training and test sets by randomlydividing the whole data set into two.

27 Jan 2023: Satellitehas a training set of 4435, a test set of 2000 with 6 classes and 36 variables. ... UCI digit data set has a trainingset of 3823, a test set of 1797, 10 classes and 64 variables.

13 Feb 2023: Additionally, for the test set, wemake use of the Negative Log Predictive Probability (NLPP). ... For training we extract 400consecutive data points, while we test on the following 500data points.

13 Feb 2023: improve the likelihood of both the training dataand of held-out test data. ... The split criterion defined by equation 3.13 can be viewed as a likelihoodratio test.

13 Feb 2023: test conditional: p(f|u) = N (K,uK1. u,uu, K, Q,) , (10b). ... Lower graph: assumption of conditional independencebetween training and test function values given u.

27 Jan 2023: IHMM evaluation in (Beal et al.,2002) is more elaborate: it allows the IHMM to con-tinue learning about new data encountered during test-ing. ... Subjecting theseresults to the same analysis as the artificial data re-veals similar compared test-set

13 Feb 2023: Table 1: Running times in seconds and test log-likelihoods for the Yale Faces dataset. ... Algorithm K Time Test Log-Likelihood. 2 56 -0.7444Finite Gibbs 5 120 -0.4220.

13 Feb 2023: We foundthat kst outperformed both competing methods sig-nificantly (p < 0.01, two sample T-test). ... 3. As measureof performance, we used average confusion, i. e. thefraction of misclassified test images.