Motivation of Chinese investment in Vietnam

China attracted a record of US$52.7×109 in foreign direct investment (FDI) in the year 2002, surpassing the United States to become the world’s largest FDI recipient. China’s success in attracting FDI has received significant attention from academics. Several theoretical approaches have been developed to explain the determinants of FDI in China. However, it seems to be ignored that China has also become a growing provider of significant FDI to the rest the world. According to United Nations Conference on Trade and Development (UNCTAD)’s 2004 report, as a developing country, replacing Japan, China has made the list of the expected top five home countries worldwide for the first time in terms of geographical coverage (2004–2005). Vietnam is second largest market and another emerging transition tiger in Southeast Asia. Both China and Vietnam were and are experiencing transitions from centrally planned economy to free market economy. This paper, therefore, attempts to explore the development of Chinese investment in Vietnam, analysing the main motives for, and characteristics of, Chinese Multinational Enterprises’ (MNEs) investment in Vietnam.

Fitness assessment of document model

Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, Resource selection for domain-specific cross-lingual IR, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification.