For each example in individual classes, the sum of the square values of the 10-dimensional feature vectors are used as samples drawn from the χ 2 distribution with 10° of freedom.
The testing-the-limits approach [ 17, 18] has been introduced in cognitive aging research as a method to use adaptive training procedures in order to estimate behavioral (cognitive) plasticity, as measured, for example, in individual differences in the maximum gain that can be induced during behavioral training in a cognitive task.
Energy surplus (for example, in individuals that are obese or suffer from metabolic syndrome) can also impair immune responses and induce chronic inflammation.
However, these conflicting data does not exclude that the met/met genotype predisposes to pain in subgroups of patients, for example in individuals with severe multifocal pain.
This occurs, for example, in individuals starting on highly active antiretroviral therapy (HAART) [ 26– 29].
For example, in individuals infected with HIV and tuberculosis, both anti-tuberculosis and antiretroviral agents have to be used [ 1].
For example, in individuals who are overweight or obese, lean body mass should be low even if absolute muscle mass (g) has not decreased.
We also established the schedule of our training process based on recent findings in neurorehabilitation showing that intensive and short-term training was beneficial [reviewed in 25], and applied, for example, in individuals with chronic aphasia [ 24].
For example, in individuals infected with HIV, the combination of KIR3DS1 and HLA-B Bw4-801 is associated with slower progression to AIDS, decreased viral loads, and fewer opportunistic infections [ 87, 88].
For example, in individuals carrying one copy of the insulin-raising C allele, the lower insulin concentration observed in association with greater whole-grain intake would be reduced by 0.009 units (that is, 0.010 units lower insulin in association with one daily whole-grain serving instead of 0.019 units lower).
The two SVM models partition the space around both hyperplanes into bins, and the distribution of the training examples in individual bins is computed.
