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This plot shows the mean
speedup in learning that is afforded by KBCC under different ten different
source-network conditions. Homogeneous subsets are indicated by brackets. No
knowledge is slowest to learn, followed by rotated components and irrelevant knowledge.
Exact individual components afford significantly faster learning. Even faster
is having both exact components (illustrated here in red, an example of which
was presented on the previous few slides). Fastest of all was having the full
exact knowledge provided by a source network that had previously learned the
cross itself.
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