@article {351,
	title = {Risk evaluation of rural financial organization operation based on the BP},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {341 - 346},
	abstract = {The 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.},
	keywords = {BP Neural Network, Risk early warning, Risk evaluation, Rural finanical},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870856910\&partnerID=40\&md5=85d32baadc606adc5051d555a7003b8a},
	author = {Fan, S. and Zhao, Y. and Xie, H. and Li, C.}
}
@article {135,
	title = {Co-training for search-based automatic image annotation},
	journal = {Journal of Digital Information Management},
	volume = {6},
	year = {2008},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2008},
	pages = {214 - 218},
	abstract = {Search-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.},
	keywords = {Automatic image annotation, Bayesian classifier, Co-training, SVM},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-72449186106\&partnerID=40\&md5=73219b4c7b5383b4a06f69f6ba3903d1},
	author = {Zhao, Y. and Zhao, Y. and Zhu, Z.}
}
