Search Results for author:
Found 3 papers, 0 papers with code
FD-FCN: 3D Fully Dense and Fully Convolutional Network for Semantic Segmentation of Brain Anatomy
Developed from the seminal FCN with an end-to-end learning-based approach and constructed by newly designed dense blocks including a dense fully-connected layer, the proposed FD-FCN is different from other FCN-based methods and leads to an outperformance in the perspective of both efficiency and accuracy.
Weighted Oblique Decision Trees
Decision trees have attracted much attention during the past decades.
On the Resistance of Nearest Neighbor to Random Noisy Labels
The theoretical results show that, for asymmetric noises, k-nearest neighbor is robust enough to classify most data correctly, except for a handful of examples, whose labels are totally misled by random noises.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
