The article explores a method called kernel Fisher discriminant for recognizing ship noises. This method involves mapping data into a different space to find a nonlinear direction for classification. By using this approach, the researchers were able to achieve better recognition results compared to other techniques. The experiment showed that the kernel Fisher discriminant, combined with a linear support vector machine, outperformed other algorithms in identifying ship noises accurately.