Entanglement and Differentiable Information Gain Maximization - Montillo, Albert; Tu, J.; Shotton, Jamie; Winn, John; E. Iglesias, J.; N. Metaxas, D.; Criminisi, Antonio (2013)
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.