When building an application that requires object class recognition, having enough data to learn from is critical for good performance, and can easily determine the success or fai...
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...
In this paper, we study a novel problem Collective Active Learning, in which we aim to select a batch set of "informative" instances from a networking data set to query ...
— The Self-Organising Map is a popular unsupervised neural network model which has successfully been used for clustering various kinds of data. To help in understanding the infl...
Discriminative approaches for human pose estimation model the functional mapping, or conditional distribution, between image features and 3D pose. Learning such multi-modal models ...