id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_o6gmakdor5esthz2os3fr7tb4q	YUVAL RAVIV	Bootstrapping with Noise: An Effective Regularization Technique	1996	20	.pdf	application/pdf	10178	1444	91	case training with noise is a very effective app roach fo r sm oothing the estim ator. noise increase s the independence betw een diffe rent training sets, we can use optim al noise levels shou ld not be based on a single estim ator perform ance, b ut · F or a noise level « j estim ate an op tim al penalty term fo r weight decay l i : differen t m easurem ents, it is best to estim ate the diffe rent noise levels in each perfo rm ance of a ® ve-n et ensem ble trained with optim al weight decay. 40-n et ensem b le ave raging resu lts, with no weight decay and no noise are better Effe ct of training with diffe rent noise levels on ® ve-n et ensem b le networks com po nents: w eight decay, noise injection and ensem ble averag ing.	./cache/work_o6gmakdor5esthz2os3fr7tb4q.pdf	./txt/work_o6gmakdor5esthz2os3fr7tb4q.txt