Deterministic neural classification.
This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN). By approximating the nonlinear counting step function using a quadratic function, the classification error rate is shown to be deterministically solvable. Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set. By considering the class-specific weights as adjustable parameters, the learning formulation extends the classification robustness of the SLFN without sacrificing its intrinsic advantage of being a closed-form algorithm. While the method is applicable to other linear formulations, our empirical results indicate SLFN's effectiveness on classification generalization.
Toh
Kar-Ann
KA
Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea. katoh@yonsei.ac.kr
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000145:classification* / Q000502:physiology; D017711:Nonlinear Dynamics