In a hybrid wired-cum-wireless network environment,packet loss may happen because of congestion or wireless link errors. Therefore,differentiating the cause is important for helping transport protocols take actions to control congestion only when the loss is caused by congestion. In this article,an end-to-end loss differentiation mechanism is proposed to improve the transmission performance of transmission control protocol (TCP)-friendly rate control (TFRC) protocol. Its key design is the introduction of the outstanding machine learning algorithm - the support vector machine (SVM) into the network domain to perform multi-metric joint loss differentiation. The SVM is characterized by using end-to-end indicators for input,such as the relative one-way trip time and the inter-arrival time of packets fore-and-aft the loss,while requiring no support from intermediate network apparatus. Simulations are carried out to evaluate the loss differentiation algorithm with various network configurations,such as with different competing flows,wireless loss rate and queue size. The results show that the proposed classifier is effective under most scenarios,and that its performance is superior to the ZigZag,mBiaz and spike (ZBS) scheme. In a hybrid wired-cum-wireless network environment, packet loss may happen because of congestion or wireless link errors. Therefore, differentiating the cause is important for helping transport protocols take actions to control congestion only when the loss is caused by congestion. article, an end-to-end loss differentiation mechanism is proposed to improve the transmission performance of transmission control protocol (TCP) -friendly rate control (TFRC) protocol. Its key design is the introduction of the outstanding machine learning algorithm - the support vector machine (SVM) into the network domain to perform multi-metric joint loss differentiation. The SVM is characterized by using end-to-end indicators for input, such as the relative one-way trip time and the inter-arrival time of packets fore -and-aft the loss, while requiring no support from intermediate network apparatus. Simulations are carried out to evaluate the loss differentiation algorithm with various network configurati ons, such as with different competing flows, wireless loss rate and queue size. The results show that the proposed classifier is effective under most scenarios, and that its performance is superior to the ZigZag, mBiaz and spike (ZBS) schemes.