@inproceedings{0c3ec6ae50a2426e94f167984e988f65,
title = "SpAM: Sparse additive models",
abstract = "We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive a method for fitting the models that is effective even when the number of covariates is larger than the sample size. A statistical analysis of the properties of SpAM is given together with empirical results on synthetic and real data, showing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data.",
author = "Pradeep Ravikumar and Han Liu and John Lafferty and Larry Wasserman",
year = "2009",
month = dec,
day = "1",
language = "English (US)",
isbn = "160560352X",
series = "Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference",
booktitle = "Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference",
note = "21st Annual Conference on Neural Information Processing Systems, NIPS 2007 ; Conference date: 03-12-2007 Through 06-12-2007",
}