Extreme　learning　machine　(ELM)　achieves　faster　training　speed　and　higher　classification　accuracy,　compared　with　other　widely　used　classifiers,　such　as　back　propagation　(BP),　support　vector　machine　(SVM),　spectral　clustering　(SC),　and　so　forth.　However,　ELM　suffers　from　some　drawbacks:　1)　ELM　utilizes　the　calculation　of　inverse　matrix　for　training,　which　cannot　be　adopted　in　the　embedded　system;　2)　the　training　time　of　ELM　increases　dramatically　for　large-scale　applications.　To　solve　these　drawbacks　of　ELM,　a　new　training　strategy　called　Sequential　ELM　(SELM)　was　proposed,　which　avoids　the　calculation　of　inverse　matrix.　Therefore,　SELM　can　be　adopted　in　the　embedded　system.　It　is　proven　that　SELM　achieves　lower　complexity　than　other　widely　used　algorithms.　Furthermore,　simulations　based　on　practical　datasets　indicate　that　the　classification　accuracy　of　SELM　is　higher　than　traditional　ELM　and　other　widely　used　classifiers　with　shorter　training　time.　©　2018,　Editorial　Department　of　Journal　of　Beijing　University　of　Posts　and　Telecommunications.　All　right　reserved.