01487nas a2200217   4500008004100000245007800041210006900119260000900188300001400197490000700211520080100218653002201019653002301041653002001064653002001084100001201104700001301116700001201129700001101141856011701152       2012                        eng d00aRisk evaluation of rural financial organization operation based on the BP0 aRisk evaluation of rural financial organization operation based   c2012  a341 - 3460 v103 aThe rural financial organization plays an important role in promoting the healthy development of the national economy especially the development of the rural economy. However, the rural financial organization risk especially the operating risk is still an outstanding problem. It has become a big obstacle in new countryside development. In this paper we analyzes the rural financial organization operational risk index firstly, then use these index as the input data for the BP neural network. After that we modeling rural financial organization operational risk early warning based the BP neural network. At last we take an experiment on a rural somewhere, the result shows that the BP neural network can reflect of the operation risk of the rural financial organization quickly and accurately.10aBP Neural Network10aRisk early warning10aRisk evaluation10aRural finanical1 aFan, S.1 aZhao, Y.1 aXie, H.1 aLi, C.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870856910&partnerID=40&md5=85d32baadc606adc5051d555a7003b8a01717nas a2200205   4500008004100000245006000041210005800101260000900159300001400168490000600182520108900188653003101277653002401308653001601332653000801348100001301356700001301369700001201382856011701394       2008                        eng d00aCo-training for search-based automatic image annotation0 aCotraining for searchbased automatic image annotation  c2008  a214 - 2180 v63 aSearch-based automatic image annotation is an effective technology to enhance the performance of annotation. By integrating the co-training technique, this paper addresses on a novel scheme for search-based image annotation, in which two classifiers can contribute to each other during the training phase. Since each classifier can select some most confident images to enhance the generalization ability of the other one, the co-training learning algorithm is triggered out for automatically mining more and more relevant images, which improve the annotation performance greatly. To characterize the various contribution of each relevant image, the probability output of the classification is taken as the corresponding weight. Moreover, the histogram of retrieved keywords is proposed to re-rank the final reliability of the keywords to be annotated, which can also guarantee the scalability property of annotation to some extent. With the promoted search precision on the basis of co-training strategy, the experimental results demonstrate the improvement of annotation performance.10aAutomatic image annotation10aBayesian classifier10aCo-training10aSVM1 aZhao, Y.1 aZhao, Y.1 aZhu, Z.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-72449186106&partnerID=40&md5=73219b4c7b5383b4a06f69f6ba3903d1