Conveners
Regression
- Amandine PIERROT (University of Bath)
We discuss the problem of active learning in regression scenarios. In active learning, the goal is to provide criteria that the learning algorithm can employ to improve its performance by actively selecting data that are most informative.
Active learning is usually thought of as being a sequential process where the training set is augmented one data point at a time. Additionally, it is...
Process stability is usually defined using iid assumption about data. However violating stability requires some concrete model like changepoint, linear trend, outliers, distributional models, positive or negative autocorrelation, etc. These violations are often tested separately and not all of the possible modes of instability can always be taken into account. We suggested a likelihood-based...
The concepts of null space (NS) and orthogonal space (OS) have been developed in independent contexts and with different purposes.
The former arises in the inversion of Partial Least Squares (PLS) regression models, as first proposed by Jaeckle & MacGregor [1], and represents a subspace in the latent space within which variations in the inputs do not affect the prediction of the outputs. The...