Soft vector quantization and the EM algorithm Export

@article{citeulike:1153131, abstract = {The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaussian mixtures (GM) have been pointed out recently by Bezdek and Pal (1995). We extend this relation to their training, showing that learning rules by these models to estimate the cluster centers can be seen as approximations to the expectation --maximization (EM) method as applied to Gaussian mixtures. HCM and...}, author = {Alpaydin, Ethem}, citeulike-article-id = {1153131}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.590}, journal = {Neural Networks}, keywords = {clustering, em, fuzzy}, number = {3}, pages = {467--477}, posted-at = {2007-03-10 21:11:59}, priority = {2}, title = {Soft vector quantization and the EM algorithm}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.590}, volume = {11}, year = {1998} }

TY - JOUR ID - citeulike:1153131 L3 - citeulike-article-id:1153131 N2 - The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaussian mixtures (GM) have been pointed out recently by Bezdek and Pal (1995). We extend this relation to their training, showing that learning rules by these models to estimate the cluster centers can be seen as approximations to the expectation --maximization (EM) method as applied to Gaussian mixtures. HCM and... IS - 3 JF - Neural Networks EP - 477 TI - Soft vector quantization and the EM algorithm VL - 11 SP - 467 KW - clustering KW - em KW - fuzzy AU - Alpaydin, Ethem PY - 1998/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.43.590 ER -