Conditional Random Sampling (CRS) was originally proposed for efficiently computing pairwise (l2, l1) distances, in static, large-scale, and sparse data. This study modifies the o...
Decision-tree algorithms are known to be unstable: small variations in the training set can result in different trees and different predictions for the same validation examples. B...
- We propose a novel framework for imitation learning that helps a humanoid robot achieve its goal of learning. There are apparent discrepancies in shapes and sizes among humans an...
Woosung Yang, Nak Young Chong, ChangHwan Kim, Bum-...
The training of support vector machines (SVM) involves a quadratic programming problem, which is often optimized by a complicated numerical solver. In this paper, we propose a muc...
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...