The PAC-learning model is distribution-independent in the sense that the learner must reach a learning goal with a limited number of labeled random examples without any prior know...
Kernel nonnegative matrix factorization (KNMF) is a recent kernel extension of NMF, where matrix factorization is carried out in a reproducing kernel Hilbert space (RKHS) with a f...
Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretabi...
Using data from an existing pre-algebra computer-based tutor, we analyzed the covariance of item-types with the goal of describing a more effective way to assign skill labels to it...
Philip I. Pavlik, Hao Cen, Lili Wu, Kenneth R. Koe...
Existing code update protocols for reprogramming nodes in a sensor network are either unsuitable or inefficient when used in a mobile environment. The prohibitive factor of uncert...