@conference {Pletscher2011a,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
@conference {Pletscher2011,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
