Uncertainty sampling optimizes an equivalent loss, enabling sample complexity analysis and asymptotic superiority guarantees over passive learning in binary classification.
Remind that the original loss can be expressed as l( ˆY·Y ) =− log ( 1 + ˆY·Y 2 )
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Understanding Uncertainty Sampling via Equivalent Loss
Uncertainty sampling optimizes an equivalent loss, enabling sample complexity analysis and asymptotic superiority guarantees over passive learning in binary classification.