@conference {hoernlein2009,
	title = {Boosting shift-invariant features},
	booktitle = {Pattern Recognition},
	volume = {5748},
	year = {2009},
	pages = {121--130},
	publisher = {Springer},
	organization = {Springer},
	abstract = {This work presents a novel method for training shift-invariant features using a Boosting framework. Features performing local convolutions followed by subsampling are used to achieve shift-invariance. Other systems using this type of features, e.g. Convolutional Neural Networks, use complex feed-forward networks with multiple layers. In contrast, the proposed system adds features one at a time using smoothing spline base classifiers. Feature training optimizes base classifier costs. Boosting sample-reweighting ensures features to be both descriptive and independent. Our system has a lower number of design parameters as comparable systems, so adapting the system to new problems is simple. Also, the stage-wise training makes it very scalable. Experimental results show the competitiveness of our approach.},
	doi = {10.1007/978-3-642-03798-6_13},
	author = {Thomas H{\"o}rnlein and Bernd J{\"a}hne and Herbert S{\"u}{\ss}e},
	editor = {Joachim Denzler and Gunther Notni}
}
