Many of the recently proposed algorithms for learning feature-based ranking functions are based on the pairwise preference framework, in which instead of taking documents in isola...
Vitor R. Carvalho, Jonathan L. Elsas, William W. C...
ibe an abstract data model of protein structures by representing the geometry of proteins using spatial data types and present a framework for fast structural similarity search bas...
We explore a stacked framework for learning to predict dependency structures for natural language sentences. A typical approach in graph-based dependency parsing has been to assum...
Kernel Principal Component Analysis (KPCA) is a popular generalization of linear PCA that allows non-linear feature extraction. In KPCA, data in the input space is mapped to highe...
This study examines the prediction of print defect perception (banding) of the human visual system (HVS) by combining detection probabilities of contrast components from wavelet a...