TY - UNPB

T1 - Active Learning of Halfspaces under a Margin Assumption

AU - Gonen, Alon

AU - Sabato, Sivan

AU - Shalev-Shwartz, Shai

PY - 2011

Y1 - 2011

N2 - We derive and analyze a new, efficient, pool-based active learning
algorithm for halfspaces, called ALuMA. Most previous algorithms show
exponential improvement in the label complexity assuming that the
distribution over the instance space is close to uniform. This
assumption rarely holds in practical applications. Instead, we study the
label complexity under a large-margin assumption -- a much more
realistic condition, as evident by the success of margin-based
algorithms such as SVM. Our algorithm is computationally efficient and
comes with formal guarantees on its label complexity. It also naturally
extends to the non-separable case and to non-linear kernels. Experiments
illustrate the clear advantage of ALuMA over other active learning
algorithms.

AB - We derive and analyze a new, efficient, pool-based active learning
algorithm for halfspaces, called ALuMA. Most previous algorithms show
exponential improvement in the label complexity assuming that the
distribution over the instance space is close to uniform. This
assumption rarely holds in practical applications. Instead, we study the
label complexity under a large-margin assumption -- a much more
realistic condition, as evident by the success of margin-based
algorithms such as SVM. Our algorithm is computationally efficient and
comes with formal guarantees on its label complexity. It also naturally
extends to the non-separable case and to non-linear kernels. Experiments
illustrate the clear advantage of ALuMA over other active learning
algorithms.

KW - Computer Science - Machine Learning

KW - Statistics - Machine Learning

M3 - פרסום מוקדם

T3 - arXiv:1112.1556 [cs.LG]

BT - Active Learning of Halfspaces under a Margin Assumption

ER -