The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf
.
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes
[article]
2010
arXiv
pre-print
Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned from independently and identically distributed (IID) data, and it is particularly so for margin classifiers: there have been recent contributions showing how practical these bounds can be either to perform model selection (Ambroladze et al., 2007) or even to directly guide the learning of linear classifiers (Germain et al., 2009). However, there are many practical situations where the training data show
arXiv:0909.1933v2
fatcat:jszzwhwlujflzem3awmlvxrbky