Now showing items 1-2 of 2
Learning without labels and nonnegative tensor factorization
(Georgia Institute of Technology, 2010-04-08)
Supervised learning tasks like building a classifier, estimating the error rate of the predictors, are typically performed with labeled data. In most cases, obtaining labeled data is costly as it requires manual labeling. ...
Learning matrix and functional models in high-dimensions
(Georgia Institute of Technology, 2014-06-20)
Statistical machine learning methods provide us with a principled framework for extracting meaningful information from noisy high-dimensional data sets. A significant feature of such procedures is that the inferences made ...