Earticle

Relational classification (RC) is concerned with the application of statistical learning to relational data. RC models do not have improved stability to smooth the perturbations generated by variations in the correlation between the relational data. Therefore, few studies have attempted to derive a bound and develop a stability learning framework for RC models. To solve this problem, we derive a learning bound with a new measure dependence stability and a limited Vapnik–Chervonenkis (VC) dimension. Based on the learning bound, we then design a stable learning framework that serves as a guideline for the development of new learning algorithms for a broad class of RC models. Applying a Markov logic network on synthesized and real-world datasets, our experimental results demonstrate that our bound can be tight if the RC model has appropriate dependence stability and limited VC dimension and our learning framework increases the stability of RC models while reducing the deviation between empirical risk and true risk.

Virtual axis machine tool is widely used in the machining process of complicated curved surface, pointing on the uncertain factors that exists in the virtual axis machine tool system will affect the process accuracy of the virtual axis machine tool, also take the problem that the upper bound of the interference of the actual system are unable to be measured into considering, in this paper, a sliding model control scheme with upper bound adaptive learning based on RBF networks, and the proposed scheme is realized in the MATLAB platform. The simulation results revealed that compared with the traditional sliding model control, the proposed control algorithm has the good performance on position tracking, the error upper bound prediction, chattering reducing, fasting convergence and so forth.

본 연구는 FDI 비율과 국내 기업의 혁신에 미치는 영향을 조직 학습 관점을 기반으로 실증 분석하였다. FDI 비율과 혁신에 대한 기존의 연구는 기업 혁신에 대한 분류가 모호하였기 때문에 상반된 연구결과들이 존재하였다. 이에 본 연구에서는 제품 혁신과 공정 혁신으로 나누어서 FDI 비율과 혁신간의 관계를 살펴봄으로써 이들 관계에 대한 이해를 보다 깊이 하고자 하였다. 따라서 본 연구는 FDI 비율이 국내 기업의 제품 공정 혁신에 긍정적인 영향을 미칠 것으로 보고, 이들의 관계를 조직 혁신이 어떻게 매개하는 지를 밝히고자 하였다. 이를 위하여, STEPI에서 제공하고 있는 2010년 2012년 제조업 분야의 기업 데이터와 산업자원통상부의 FDI 데이터, 그리고 NICE 신용평가원(Kis Value)의 기업재무자료를 결합하여 실증 분석을 실시하였다. 분석 결과 FDI 비율은 국내 기업의 제품 혁신에 긍정적인 영향을 미치고 있는 것으로 나타났다. 또한 FDI 비율과 제품 혁신 간의 관계에 대하여 조직 혁신이 매개 효과를 가지는 것을 확인할 수 있었다.

FDI has been considered as a source of competitive advantage by bringing scientific and technological innovation capabilities to domestic firms via organizational learning. Acquiring knowledge and technology by learning accelerates firms to be innovative. In the way of innovation, firms seek for innovation as a whole but innovation can be clarified as two different parts, product and process innovation. Different from product and process innovation, organizational innovation is not directly related to productivity or outcome but it is closely related to product and process innovations. As a kind of firm innovation, organizational innovation may be considered as preceding product and process innovation and it may positively mediate the relationship between in-bound FDI and firms' product and process innovations. In this paper, the relationship between FDI and product and process innovation will be explained by organizational learning and the way of organizational innovation affects to the relationship will be examined.