Ranking problems, also known as preference learning problems, define a widely spread
class of statistical learning problems with many applications, including fraud detection …

Reliable estimation of the classification performance of inferred predictive models is difficult
when working with small data sets. Cross-validation is in this case a typical strategy for …

How should affect be appropriately annotated and how should machine learning best be
employed to map manifestations of affect to affect annotations? What is the use of ratings of …

Learning to rank is an increasingly important scientific field that comprises the use of
machine learning for the ranking task. New learning to rank methods are generally …

Multimodal datasets often feature a combination of continuous signals and a series of
discrete events. For instance, when studying human behaviour it is common to annotate …

Reliable estimation of the classification performance of learned predictive models is difficult,
when working in the small sample setting. When dealing with biological data it is often the …

In this paper, we propose a listwise approach for constructing user-specific rankings in
recommendation systems in a collaborative fashion. We contrast the listwise approach to …

Implicit feedback collaborative filtering has attracted a lot of attention in collaborative
filtering, which is called one-class collaborative filtering (OCCF). However, the low …

RLScore is a Python open source module for kernel based machine learning. The library
provides implementations of several regularized least-squares (RLS) type of learners. RLS …

Recently, Extreme learning machine (ELM), an efficient training algorithm for single-hidden-
layer feedforward neural networks (SLFN), has gained increasing popularity in machine …