Abstract:
Kernel-based extreme learning machine (KELM), a natural extension of ELM to kernel learning, excels in regression and classification problems. Due to its random projection mechanism and lack of hidden node requirements, KELM generalizes better than ELM. KELM’s performance may suffer in non-Gaussian cases because it is derived using the minimum mean square error (MMSE) criterion for Gaussian noise. This article proposes a mixture correntropy-based KELM (MC-KELM) instead of MMSE to improve KELM’s robustness. MCOS-KELM, an online sequential version of MC-KELM, handles sequential data. Experimental regression and classification data sets demonstrate the new methods’ performance advantages.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
