Suvi Alenius, Eero Kajantie, Reijo Sund, Markku Nurhonen, Peija Haaramo, Pieta Näsänen-Gilmore, Sakari Lemola, Katri Räikkönen, Daniel D Schnitzlein, Dieter Wolke, Mika Gissler, Petteri Hovi
School grades and educational attainments of adolescents and young adults born preterm
Scientific reports
ensuring prediction accuracy learning algorithm theoretical basis crucial necessary building reliability learning algorithm paper analyzes prediction error obtained least square estimation generalized extreme learning machine gelm applies limiting behavior moorepenrose generalized inverse mp gi output matrix elm elm random vector functional link rvfl network without direct input output links specifically analyze tail probabilities associated upper lower bounds error expressed norms analysis employs concepts l2 norm frobenius norm stable rank mp gi coverage theoretical analysis extends rvfl network addition criterion precise bounds prediction errors may give stochastically better network environments provided analysis applied simple examples largesize datasets illustrate procedure verify analysis execution speed big data based study immediately obtain upper lower bounds prediction errors associated tail probabilities matrices calculations appearing gelm rvfl analysis provides criteria reliability learning performance network realtime network structure enables obtaining better performance reliability analysis applied various areas elm rvfl adopted proposed analytical method guide theoretical analysis errors occurring dnns employ gradient descent algorithm