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Summarized, the toy data can be varied in the dimension D, the number of tasks T, the number of training instances per task N, and the similarity between the tasks σ2=s β.

Each criterion is mapped to a single training instance.

The training time of both algorithms is linear in the number of training instances and tasks.

In every repetition the training samples were randomly selected from the complete set of training instances.

The N training instances were sampled separately for each task.

Uncovered test instances are assigned the default class value (or distribution) of the uncovered training instances.

(a) First, the root model is trained taking into account all training instances.

First, training instances are collected in a dictionary.

These instances amount to 84.3% of the total (6 547 124) training instances.

During testing, test instances were created in the same way as the negative training instances.

After creating the training instances, we train a 11-class classifier on them using SVMmulticlass (15).

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