John Steinbeck, who has won the Nobel Prize because of his ＂Grapes of Wrath＂, is one of the most famous America novelists. This article pays special attention on how does Taoist Oriental civilization save the declin...John Steinbeck, who has won the Nobel Prize because of his ＂Grapes of Wrath＂, is one of the most famous America novelists. This article pays special attention on how does Taoist Oriental civilization save the decline of Western spiritual world, and the ＂Eastern＂ color, as well as the concept of good and evil conflict and the coincidence of Steinbeck＇s theme with Chinese culture. In this sense, Steinbeck＇s works have a great association with the oriental.展开更多
We continue our study on classification learning algorithms generated by Tikhonov regularization schemes associated with Gaussian kernels and general convex loss functions. Our main purpose of this paper is to improve...We continue our study on classification learning algorithms generated by Tikhonov regularization schemes associated with Gaussian kernels and general convex loss functions. Our main purpose of this paper is to improve error bounds by presenting a new comparison theorem associated with general convex loss functions and Tsybakov noise conditions. Some concrete examples are provided to illustrate the improved learning rates which demonstrate the effect of various loss functions for learning algorithms. In our analysis, the convexity of the loss functions plays a central role.展开更多
文摘John Steinbeck, who has won the Nobel Prize because of his ＂Grapes of Wrath＂, is one of the most famous America novelists. This article pays special attention on how does Taoist Oriental civilization save the decline of Western spiritual world, and the ＂Eastern＂ color, as well as the concept of good and evil conflict and the coincidence of Steinbeck＇s theme with Chinese culture. In this sense, Steinbeck＇s works have a great association with the oriental.
文摘We continue our study on classification learning algorithms generated by Tikhonov regularization schemes associated with Gaussian kernels and general convex loss functions. Our main purpose of this paper is to improve error bounds by presenting a new comparison theorem associated with general convex loss functions and Tsybakov noise conditions. Some concrete examples are provided to illustrate the improved learning rates which demonstrate the effect of various loss functions for learning algorithms. In our analysis, the convexity of the loss functions plays a central role.
