Marxist Historiographies is the first book to examine the ebb and flow of Marxist historiography from a global and cross-cultural perspective. Since the eighteenth century, few schools of historical thought have exerted a more lasting impact than Marxism, and this impact extends far beyond the Western world within which it is most commonly analysed. Edited by two highly respected authors in the field and taking a truly global perspective on this topic, Marxist Historiographies demonstrates clearly the breadth and depth of (...) Marxism's influence in historical writing throughout the world and is essential reading for all students of historiography. (shrink)

In the May Fourth era of the 1910s and 1920s, Confucius' image was associated closely with the conservative political forces that were increasingly regarded by many as the cause of the challenges the newly founded Republic was facing. With respect to Confucius, Hu Shi believed that the critical attitude engendered in the May Fourth era had brought Confucius down from a high pedestal and put him on a par with the contemporaries of his time. Hu Shi's main assessment of Confucius (...) as an ancient philosopher was shown in his doctoral dissertation and the later published A History of Ancient Chinese Philosophy, both appeared in the May Fourth era. During the May Fourth era, the interest in distinguishing “historical Confucius” from “iconic Confucius” was conducive to the emergence of the National Studies Movement, led by Zhang Taiyan, Hu Shi, Liang Qichao, and others. (shrink)

Taking Chun-chieh Huang’s ruminations on the defining character of Chinese historical thinking as a starting point, this essay discusses the ways in which historical cultures and traditions are compared and contrasted and explores some new ways of thinking. It argues that cultural comparisons often constitute two-way traffic and that attempts to characterize one historical culture, such as that of China, are often made relationally and temporally. When the Chinese tradition of historiography is perceived and presented in the West, it has (...) been regarded more or less as a counterexample against which the “unique” traits of Western historical thinking are thrown into relief. Given the hegemonic influence of Western scholarship in modern times, latter-day Chinese historians also valorize the East–West dichotomy. A closer look at this dichotomy, or the characterization of both cultures, reveals that it is not only relative but also relational and temporal. When the modern Chinese appeared impressed by the rigor of Rankean critical historiography, for example, they were essentially attempting to rediscover their own cultural past, for example, the eighteenth-century tradition of evidential learning, in adapting to the changing world. Our task today, the essay contends, is to historicize the specific context within which cultural comparisons are made and to go beyond readily accepted characterizations in order to reassess certain elements in a given culture, to apply historical wisdom, and to cope with the challenges we now face. (shrink)

Examining turning points in historical thought in a variety of cultures, the essay here deal with reorientations in historical thinking in the pre-modern period since Antiquity, mainly in ancient Greece and China and in medieval Christian Europe.

Smooth function is the essence of the ε-smooth support vector regression (ε-SSVR) which is an unconstrained convex quadratic programming. In this paper, Bézier function as a new smooth function is proposed to replace the ε-insensitive loss function of ε-SSVR with tolerating a smaller error in fitting given data sets linearly and nonlinearly. The ε-Bézier function smooth support vector regression (ε-BSSVR) model is presented. Theoretical analyses show that the derived smooth function has improved approximation precision compared with other smooth approximate functions. (...) The Newton-Armijo algorithm is applied to solve the new model. Numerical results and comparisons demonstrate that the proposed method has the better regression performance and learning efficiency compared with other given models. (shrink)

A new smooth method for solving the support vector machine classification (SVC) is presented. Since the objective function of the unconstrained SVC is non-smooth, we apply the smooth technique and replace the SVC function with Bézier function and get a class of Bézier smooth support vector machines (BSSVM). The fast Newton-Armijo algorithm is used to solve the BSSVM. Theoretical analysis and numerical results illustrate that this smooth SVM model improves in efficiency and accuracy compared with other smooth methods.

ε-support vector regression (ε-SVR) as a constrained minimization problem can be converted into an unconstrained convex quadratic programming. Smooth function is the essence of the ε-smooth support vector regression (ε-SSVR). In this paper, a new rotated hyperbola function is proposed to replace the ε-insensitive loss function. The ε-rotated hyperbola smooth support vector regression (ε-RHSSVR) model is presented. Theoretical analyses show that the derived smooth function has improved approximation precision compared with other smooth approximate functions. The Newton-Armijo algorithm is applied to (...) train the new model. Numerical results and comparisons are given to demonstrate the effectiveness and speed of the method. -/- . (shrink)

Volume 20, Issue 9, September 2020, Page 39-41.

This article puts forward a novel class of Bézier smooth semi-supervised support vector machines(BS4VMs) for classification. As is well known, semi-supervised support vector machine is introduced for dealing with quantities of unlabeled data in the real world. Labeled data is utilized to train the algorithm and then adapting it to classify the unlabeled data. However, the objective semi-supervised function is not differentiable globally. It is required to endure heavy burden in solving two quadratic programming problems with inversion matrix operation. To (...) solve this problem, the interpolation Bézier functions for replacing the non-smooth term have been investigated. Firstly, the development of the novel BS4VMs classifier is introduced. Then, the non-linear model and convergence analysis are presented. To show how the BS4VMs can be practically implemented, experiments on synthetic and real data sets are offered. The theoretical analysis and experiments comparisons clearly illustrate the BS4VMs are superior to the proposed methods in both cross-validation classification accuracy and calculating time. (shrink)