Abstract
A modified version of Neural Decision Tree (NDT) which was initially proposed by Biau is presented in this paper with significantly extended statistical consistency theoretical results. Numerical experiments are performed to investigate several key features of this alternative NDT. Our experimental results demonstrate the attractiveness of NDT over the traditional artificial neural networks by adopting a strategically selected network structure with appropriate initial weights. Moreover, our results indicate that in all cases we have tested, NDT provides higher classification accuracy than a traditional neural network with a comparable degree of freedom. Empirical evidence also suggests that NDT is more stable and robust with respect to hyper-parameters commonly involved in the training of neural networks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
