@misc{montillo2013entanglement,
author = {Montillo, Albert and Tu, J. and Shotton, Jamie and Winn, John and E. Iglesias, J. and N. Metaxas, D. and Criminisi, Antonio},
title = {Entanglement and Differentiable Information Gain Maximization},
year = {2013},
month = {January},
abstract = {Decision forests can be thought of as a flexible optimization toolbox with many avenues to alter or recombine the underlying architectural components and improve recognition accuracy and efficiency. In this chapter, we present two fundamental approaches for re-architecting decision forests that yield higher prediction accuracy and shortened decision time.},
url = {http://approjects.co.za/?big=en-us/research/publication/entanglement-and-differentiable-information-gain-maximization-2/},
}