This paper introduces a class of k-nearest neighbor (k-NN) estimators called bipartite plug-in (BPI) estimators for estimating integrals of non-linear functions of a probability density, such as Shannon entropy and R´enyi entropy. The density is assumed to be smooth, have bounded support, and be uniformly bounded from below on this...
Novelty detection plays an important role in machine learning and signal processing. This
project studies novelty detection in a new setting where the data object is represented as
a bag of instances and associated with multiple class labels, referred to as multi-instance
multi-label (MIML) learning. Contrary to the common assumption...
The development of ecohydrological frameworks and theories under the ongoing global climate crisis depends on the development of new and advanced ecohydrological measurements. Currently, numerous of datasets have been collected at plot and ecosystem levels to understand the complex interactions of along the soil, plant, and atmosphere continuum. The development...
In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...