Bandit algorithms are concerned with trading exploration with exploitation where a number of options are available but we can only learn their quality by experimenting with them. ...
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 ...
Antti Airola, Tapio Pahikkala, Willem Waegeman, Be...
Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Col...
Walter S. Lasecki, Jeffrey P. Bigham, James F. All...
We address a new perceptual grouping algorithmfor aerial images, which employs a decision tree classifier and hierarchical multilevel grouping strategy an a bottom-up fashion. In ...
Online Boosting is an effective incremental learning method which can update weak classifiers efficiently according to the object being trackedt. It is a promising technique for o...