Incorporating prior information in machine learning by creating virt ual examples Export

@misc{citeulike:2140004, abstract = {One of the key problems in supervised learning is the insufficient size of the training set. The natural way for an intelligent learner to counter this problem and successfully generalize is to exploit prior information that may be available about the domain or that can be learned from prototypical examples. We discuss the notion of using prior knowledge by creating virtual examples and thereby expanding the effective training set size. We show that in some contexts, this idea is mathematically ...}, author = {Niyogi, P. and Girosi, F. and Poggio, T.}, citeulike-article-id = {2140004}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.9531}, posted-at = {2007-12-18 08:45:54}, priority = {2}, title = {Incorporating prior information in machine learning by creating virt ual examples}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.9531}, year = {1998} }

TY - GEN ID - citeulike:2140004 L3 - citeulike-article-id:2140004 N2 - One of the key problems in supervised learning is the insufficient size of the training set. The natural way for an intelligent learner to counter this problem and successfully generalize is to exploit prior information that may be available about the domain or that can be learned from prototypical examples. We discuss the notion of using prior knowledge by creating virtual examples and thereby expanding the effective training set size. We show that in some contexts, this idea is mathematically ... TI - Incorporating prior information in machine learning by creating virt ual examples AU - Niyogi, P AU - Girosi, F AU - Poggio, T PY - 1998/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.9531 ER -