anytime algorithms for learning anytime classifiers saher ... - Technion
anytime algorithms for learning anytime classifiers saher ... - Technion
anytime algorithms for learning anytime classifiers saher ... - Technion
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<strong>Technion</strong> - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008<br />
Average size<br />
Average accuracy<br />
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Time [sec]<br />
LSID3<br />
LSID3-MC<br />
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Time [sec]<br />
LSID3<br />
LSID3-MC<br />
Figure 3.27: Anytime behavior of LSID3-MC on the Numeric-XOR 4D dataset<br />
repetitions, and there<strong>for</strong>e more resources were devoted to more promising<br />
tests rather than one repetition <strong>for</strong> each point as in LSID3(r = 1).<br />
4. When the available time is insufficient <strong>for</strong> running LSDI3(r = 1) but more<br />
than sufficient <strong>for</strong> running ID3, LSID3-MC is more flexible and allows these<br />
intermediate points of time to be exploited. For instance, by using only<br />
one-fifth of the time required by LSID3(r = 1), an absolute accuracy improvement<br />
of 20% over ID3 was achieved.<br />
3.7.4 Anytime behavior of IIDT<br />
IIDT was presented as an interruptible decision tree learner that does not require<br />
advanced knowledge of its resource allocation: it can be stopped at any moment<br />
and return a valid decision tree. We tested two versions of IIDT, the first with<br />
granularity threshold g = 0.1 and the second with g = 1. Figures 3.28, 3.29, and<br />
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