Feature Discovery for Inductive Concept Learning Export

@techreport{citeulike:665808, abstract = {ions are created by relaxing conditions specified in a domain theory, so the approach is a hybrid of the data-driven (bottom-up) and theory-driven (top-down) approaches. The use of a domain theory allows the system to start with complex initial features that are sensitive to the goals and operators of the domain, rather than simply starting with the instance-level features. The domain theory also allows the system to perform goal regression, a powerful goal-based feature transformation....}, author = {Fawcett, Tom E.}, citeulike-article-id = {665808}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.4448}, keywords = {ai, feature\_extraction, games, general\_game\_playing, logic}, month = {,}, number = {UM-CS-1990-015}, posted-at = {2006-05-22 22:39:26}, priority = {0}, title = {Feature Discovery for Inductive Concept Learning}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.4448}, year = {1990} }

TY - RPRT ID - citeulike:665808 L3 - citeulike-article-id:665808 N2 - ions are created by relaxing conditions specified in a domain theory, so the approach is a hybrid of the data-driven (bottom-up) and theory-driven (top-down) approaches. The use of a domain theory allows the system to start with complex initial features that are sensitive to the goals and operators of the domain, rather than simply starting with the instance-level features. The domain theory also allows the system to perform goal regression, a powerful goal-based feature transformation.... IS - UM-CS-1990-015 TI - Feature Discovery for Inductive Concept Learning KW - ai KW - feature_extraction KW - games KW - general_game_playing KW - logic AU - Fawcett, Tom PY - 1990/,// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.4448 ER -