In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending
on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets
indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
漢中路到了。開左邊門，下車請注意安全。We are now at Hanzhong Road . Doors will open on the left。
本次列車終點站上海火車站。下一站終點站上海火車站，開左邊門。使用公交卡的乘客可在出站后30分鐘內換乘3號線、4號線，請注意換成列車的首末班車時間。打開metro大都會手機數碼乘地鐵。
Nest stop is the termina.station ShanghaiRailway station.Roors will open on the lift.
終點站上海火車站到了。開左邊門。下車請注意安全。請全體乘客下車。We are now at the termina.station Shanghai
Railway station Roors will open on the lift.
