text: CONTRIBUTION The main contribution of the paper is not clearly stated.   To the reviewer, It seems “life-long learning” is the same as “online learning”.   However, the whole paper does not define what “life-long learning” is. The limited budget scheme is well established in the literature.  1. J. Hu, H. Yang, I. King, M. R. Lyu, and A. M.-C. So. Kernelized online imbalanced learning with fixed budgets. In AAAI, Austin Texas, USA, Jan. 25-30 2015.   2. Y. Engel, S. Mannor, and R. Meir. The kernel recursive least-squares algorithm. IEEE Transactions on Signal Processing, 52(8):2275–2285, 2004. It is not clear what the new proposal in the paper. WRITING QUALITY The paper is not well written in a good shape.  Many meanings of the equations are not stated clearly, e.g., $phi$ in eq. (7).  Furthermore, the equation in algorithm 2 is not well formatted.  DETAILED COMMENTS 1. The mapping function $phi$ appears in Eq. (1) without definition. 2. The last equation in pp. 3 defines the decision function f by an inner product.  In the equation, the notation x_t and i_t is not clearly defined.   More seriously, a comma is missed in the definition of the inner product. 3. Some equations are labeled but never referenced, e.g., Eq. (4). 4. The physical meaning of Eq.(7) is unclear.   However, this equation is the key proposal of the paper.    For example, what is the output of the Eq. (7)?  What is the main objective of Eq. (7)?   Moreover, what support vectors should be removed by optimizing Eq. (7)?   One main issue is that the notation $phi$ is not clearly defined.    The computation of f-y_r\phi(s_r) makes it hard to understand.   Especially,  the dimension of $phi$ in Eq.(7) is unknown.  ABOUT EXPERIMENTS 1.	It is unclear how to tune the hyperparameters. 2.	In Table 1, the results only report the standard deviation of AUC.  No standard deviations of nSV and Time are reported.