In 2014, the above paper entitled ‘Quasi-Linear Support Vector Machine for Nonlinear Classification’ was published by Zhou, et al. [1]. They proposed a quasi-linear kernel function for support vector machine (SVM). However, in this letter, we point out that this proposed kernel function is a part of multiple kernel functions generated by well-known multiple kernel learning which is proposed by Bach, et al. [2] in 2004. Since then, there have been a lot of related papers on multiple kernel learning with several applications [3]. This letter verifies that the main kernel function proposed by Zhou, et al. [1] can be derived using multiple kernel learning algorithms [3]. In the kernel construction, Zhou, et al. [1] used Gaussian kernels, but the multiple kernel learning had already discussed the locality of additive Gaussian kernels or other kernels in the framework [4], [5]. Especially additive Gaussian or other kernels were discussed in tutorial at major international conference ECCV2012 [6]. The authors did not discuss these matters.

Agent-based computing has been advocated by many researchers and software developers as a promising and innovative way to model, design and implement complex Web-related applications. KODAMA (Kyushu university Open & Distributed Autonomous MultiAgent) project, which is described in this paper, is our endeavour to advance both the technology and engineering of well-known multiagent systems. In particular, we have noted that software agents might be the potential solution to many problems faced by today's Web. However, building a high-quality, large-scale multiagent system, which can operate in open environments, is a great challenge. So far, we have devoted a lot of effort to design and implement a generic agent architecture, a hierarchical agent community structure, and an independent network communication middleware. To ensure that KODAMA can be used to create Web-agent applications, its network communication performance and a prototype distributed database retrieval system have been tested. The result shows that KODAMA is suitable for developing network-aware applications.