We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
Some of the most successful algorithms for the automated segmentation of images use an Active Regions approach, where a curve is evolved so as to maximize the disparity of its int...
Gregory Randall, Juan Cardelino, Marcelo Bertalm&i...
The problem of discovering arrangements of regions of high occurrence of one or more items of a given alphabet in a sequence, is studied, and two efficient approaches are propose...
Panagiotis Papapetrou, Gary Benson, George Kollios
Background: Support Vector Machines (SVMs) ? using a variety of string kernels ? have been successfully applied to biological sequence classification problems. While SVMs achieve ...
Background: The classification of protein sequences using string algorithms provides valuable insights for protein function prediction. Several methods, based on a variety of diff...