TY - GEN
T1 - A method for inferring label sampling mechanisms in semi-supervised learning
AU - Rosset, Saharon
AU - Zhu, Ji
AU - Zou, Hui
AU - Hastie, Trevor
PY - 2005
Y1 - 2005
N2 - We consider the situation in semi-supervised learning, where the "label sampling" mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of moments for estimating this stochastic dependence using the unlabeled data. This is potentially useful for two distinct purposes: A. As an input to a supervised learning procedure which can be used to "de-bias" its results using labeled data only and b. As a potentially interesting learning task in itself. We present several examples to illustrate the practical usefulness of our method.
AB - We consider the situation in semi-supervised learning, where the "label sampling" mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of moments for estimating this stochastic dependence using the unlabeled data. This is potentially useful for two distinct purposes: A. As an input to a supervised learning procedure which can be used to "de-bias" its results using labeled data only and b. As a potentially interesting learning task in itself. We present several examples to illustrate the practical usefulness of our method.
UR - http://www.scopus.com/inward/record.url?scp=33749554765&partnerID=8YFLogxK
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AN - SCOPUS:33749554765
SN - 0262195348
SN - 9780262195348
T3 - Advances in Neural Information Processing Systems
BT - Advances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
PB - Neural information processing systems foundation
T2 - 18th Annual Conference on Neural Information Processing Systems, NIPS 2004
Y2 - 13 December 2004 through 16 December 2004
ER -