Incremental Online Learning in High Dimensions Export

@misc{citeulike:2245498, abstract = {Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally e\#cient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least...}, author = {Vijayakumar, Sethu and D'Souza, Aaron and Schaal, Stefan}, citeulike-article-id = {2245498}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.8649}, keywords = {function\_approximation, input\_selection, online}, posted-at = {2008-01-17 14:34:44}, priority = {4}, title = {Incremental Online Learning in High Dimensions}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.8649} }

TY - GEN ID - citeulike:2245498 L3 - citeulike-article-id:2245498 N2 - Locally weighted projection regression (LWPR) is a new algorithm for incremental nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its core, it employs nonparametric regression with locally linear models. In order to stay computationally e#cient and numerically robust, each local model performs the regression analysis with a small number of univariate regressions in selected directions in input space in the spirit of partial least... TI - Incremental Online Learning in High Dimensions KW - function_approximation KW - input_selection KW - online AU - Vijayakumar, Sethu AU - D'Souza, Aaron AU - Schaal, Stefan UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.8649 ER -