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Gaussian Processes for Ordinal Regression

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Figure

Figure 1: The graph of the likelihood function for an ordinal regression problem with r = 3, alongwith the first and second order derivatives of the loss function (negative logarithm of thelikelihood function), where the noise variance σ2 = 1, and the two thresholds are b1 = −3and b2 = +3.
Figure 2: The performance of the three algorithms on a synthetic three-rank ordinal regressionproblem
Table 1: Data sets and their characteristics. “Attributes” state the number of numerical and nominalattributes
Table 3: Test results of the three algorithms using a Gaussian kernel. The targets of these bench-mark data sets were discretized by 10 equal-length bins
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