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icm model 0.0022074620000000003
model 0.00197531
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data chap 0.0012370929999999999
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full task 0.001162755
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vector machine 9.99054E-4
modelling text 9.98892E-4
coregionalisation matrix 9.94106E-4
tion matrix 9.9119E-4
sis function 9.88557E-4
language processing 9.79249E-4
natural language 9.62558E-4
other tasks 9.51558E-4
kernel 9.33404E-4
covariance matrix 9.324590000000001E-4
different parameterisation 9.282450000000001E-4
advanced vector 9.23693E-4
modelling approach 9.22466E-4
language sen 9.095549999999999E-4
gram matrix 8.973270000000001E-4
support vector 8.86995E-4
matrix corre 8.818960000000001E-4
matrix uλut 8.78796E-4
nal matrix 8.77444E-4
isation matrix 8.755010000000001E-4
same value 8.53339E-4
translation quality 8.517010000000001E-4
chical kernels 8.390229999999999E-4
test input 8.2946E-4
score distribution 8.230749999999999E-4
gaussian likelihood 8.16041E-4
multiple domains 8.1373E-4
other emotions 8.119080000000001E-4
other hand 8.10548E-4
error analysis 8.101779999999999E-4
multiple emotions 8.0992E-4
multiple annotators 8.02939E-4
multiple layers 8.02939E-4
coregionalisation approach 8.02654E-4
noise modelling 7.96017E-4
text periodicities 7.890659999999999E-4
svm performance 7.8811E-4
same behaviour 7.827940000000001E-4
prediction results 7.77221E-4
prior assumptions 7.76091E-4
prediction values 7.743369999999999E-4
results table 7.666890000000001E-4
same cluster 7.63533E-4
bayesian posterior 7.63526E-4
function 7.62807E-4
set size 7.62303E-4
metadata information 7.60245E-4
ing scores 7.5575E-4
lihood distribution 7.542779999999999E-4
research avenue 7.53283E-4
test score 7.52257E-4
gaussian distri 7.412440000000001E-4
log likelihood 7.30857E-4
gaussian likelihoods 7.30645E-4
svm baseline 7.26356E-4
gaussian processes 7.248910000000001E-4
small datasets 7.24004E-4
ing rank 7.21101E-4
training 7.19571E-4
beta distribution 7.178740000000001E-4
future work 7.153179999999999E-4
