We present the first unsupervised approach to the problem of learning a semantic parser, using Markov logic. Our USP system transforms dependency trees into quasi-logical forms, r...
We present an efficient, fully automated algorithm to assemble ESTs into full-length cDNA sequences that represent the complete coding regions of a gene. Our EST clustering algori...
Arthur Grossman, Charles Hauser, Hilary J. Holz, J...
We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of s...
We propose a fuzzy logic recursive scheme for motion detection and spatiotemporal filtering that can deal with the Gaussian noise and unsteady illumination conditions in both the t...
Vladimir Zlokolica, Aleksandra Pizurica, Wilfried ...
We propose Merge Growing Neural Gas (MGNG) as a novel unsupervised growing neural network for time series analysis. MGNG combines the state-of-the-art recursive temporal context of...
Andreas Andreakis, Nicolai von Hoyningen-Huene, Mi...