Learning qualitative models of dynamic systems

@article{citeulike:436909, abstract = {. The automated construction of dynamic system models is an important application area for ILP. We describe a method that learns qualitative models from time-varying physiological signals. The goal is to understand the complexity of the learning task when faced with numerical data, what signal processing techniques are required, and how this affects learning. The qualitative representation is based on Kuipers' Qsim. The learning algorithm for model construction is based on Coiera's Genmodel. We ...}, address = {Johns Hopkins Univ, Sch Med, Baltimore, Md, 21205 Hewlett Packard Labs, Bristol Bs12 6Qz, Avon, England}, author = {Hau, D. T. and Coiera, E. W. }, citeulike-article-id = {436909}, journal = {Machine Learning}, keywords = {qualitative}, pages = {177--211}, posted-at = {2007-01-22 14:18:02}, priority = {2}, title = {Learning qualitative models of dynamic systems}, url = {http://citeseer.ist.psu.edu/hau97learning.html}, volume = {26}, year = {1997} }

TY - JOUR ID - citeulike:436909 L3 - citeulike-article-id:436909 TI - Learning qualitative models of dynamic systems JF - Machine Learning VL - 26 SP - 177 EP - 211 AD - Johns Hopkins Univ, Sch Med, Baltimore, Md, 21205 Hewlett Packard Labs, Bristol Bs12 6Qz, Avon, England N2 - . The automated construction of dynamic system models is an important application area for ILP. We describe a method that learns qualitative models from time-varying physiological signals. The goal is to understand the complexity of the learning task when faced with numerical data, what signal processing techniques are required, and how this affects learning. The qualitative representation is based on Kuipers' Qsim. The learning algorithm for model construction is based on Coiera's Genmodel. We ... KW - qualitative AU - Hau, DT AU - Coiera, EW PY - 1997/// UR - http://citeseer.ist.psu.edu/hau97learning.html ER -