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Unsupervised nonparametric density estimation: A neural network approach
2009
2009 International Joint Conference on Neural Networks
One major problem in pattern recognition is estimating probability density functions. Unfortunately, parametric techniques rely on an arbitrary assumption on the form of the underlying, unknown density function. On the other hand, nonparametric techniques, such as the popular kn-Nearest Neighbor (not to be confused with the k-Nearest Neighbor classification algorithm), allow to remove such an assumption. Albeit effective, the kn-Nearest Neighbor is affected by a number of limitations.
doi:10.1109/ijcnn.2009.5179010
dblp:conf/ijcnn/TrentinF09
fatcat:f6gvsc44arhp5hrn5k56xeklo4