Dec
6
“In pattern recognition, the classifier should be designed by using samples near the decision boundary; samples far from the decision boundary are less important to the design. However, if we fix the desired output gamma(X) and try to minimize the mean-square error between h(X) and gamma(X), larger h(X)’s contribute more to the mean-square error. This has long been recognized as a disadvantage of a mean-square error approach in pattern recognition.”
Keinosuke Fukunaga
