@article {400,
	title = {A novel fault identifying method with supervised classification and unsupervised clustering},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {184 - 189},
	abstract = {To satisfy the robust requirement when designing fault identifying method, this paper proposes a novel method to identify sensor fault. Conventional fault identifying method could only classify fault into explicit set. Yet, when a novel faulty pattern occurs, the conventional method can not identify this new pattern and will classify it into a set known ahead of time. For the purpose of robustness of fault identifying method, the supervised classification and the unsupervised clustering are integrated together. Faulty features are extracted with wavelet package decomposition to train the supervised classification algorithm and the unsupervised clustering algorithm. As the supervised classification method, Support Vector Machine (SVM) is utilized to classify those faulty patterns known ahead of time into explicit set for the purpose of identifying faulty pattern. As the unsupervised clustering method, subtractive clustering method is utilized to identify the novel fault pattern. Therefore, SVM and subtractive clustering are integrated to identify sensor fault pattern even when novel faulty pattern emerges. The applicability and effectiveness of the proposed method is illustrated by flow sensor data in an engine fuel providing system. The result shows that the method adopted provides better performance compared with conventional method while satisfying the robust requirement of fault identifying method.},
	keywords = {Fault identify, Subtractive clustering, Supervised classification, Support vector machine, Unsupervised clustering},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84880836927\&partnerID=40\&md5=9f7c84bdac0bd7c607dac64aa37c7362},
	author = {Xu, T.}
}
@article {384,
	title = {Optimization and application of support vector machine based on SVM algorithm parameters},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {165 - 169},
	abstract = {The hospital customer classification is very important for the integration and allocation of hospital resources, can greatly enhance the market competitiveness of the hospital. Support Vector Machine (SVM) is an approach to solve classification problem by using optimization method. Selecting different kernel parameters can construct different classifiers, meanwhile parameters decide their learning and generalization ability. In order to solve the limitation of selecting parameters by experience, so the particle swarm (PSO) pattern search algorithm is proposed to search optimal parameters and take them into the practice of hospital customer classification; The PSO mode search algorithm is to combine the advantages of PSO algorithm and pattern search algorithm, PSO mode search algorithm has strong global search capability and good advantage of local convergence. The result of experiment shows that this method is not only efficient, but also to search the optimal parameters achieving a high accuracy, which is an effective method of SVM parameter optimization.},
	keywords = {Customer data classification, Kernel parameters selection, Particle swarm pattern search, Support vector machine},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879098093\&partnerID=40\&md5=aa26078b417c2386e1244d36d3bacb09},
	author = {Yan, H.-F. and Wang, W.-F. and Liu, J.}
}
@article {330,
	title = {Experimental evaluation for impact factors of online signature verification},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2012},
	pages = {187 - 193},
	abstract = {A study of online signature verification has been carried out based on experimental evaluation. We started by presenting a brief survey of the recent literatures on important factors of online signature verification. Four main issues have been addressed in the work: 1) assessment for signature consistency caused by different physiological factor. 2) Analysis for variation and complexity of online signature. 3) Performance evaluation for features widely used in several datasets. 4) Comparative analysis for algorithms applied on acquired datasets. From detailed experimental analysis, we identified and characterized some important aspects of online signatures.},
	keywords = {Biometrics, Dynamic timing warping, Experimental evaluation, Online signature verification, Support vector machine},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866459887\&partnerID=40\&md5=efb57c0a5eb8df586d615ad81371552d},
	author = {Hao, C. and Ming, D.D.}
}
@article {333,
	title = {Word combination kernel for text categorization},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {202 - 211},
	abstract = {We proposed a novel kernel for text categorization. This kernel is an inner product in the feature space generated by all word combinations of specified length. A word combination is a collection of different words co-occurring in the same sentence. The word combination of length k is weighted by the k-th root of the product of the inverse document frequencies (IDF) of its words. A computationally simple and efficient algorithm was proposed to calculate this kernel. By restricting the words of a word combination to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the dimensionality this kernel can be reduced significantly compared to the word-sequence kernel. We conducted a series of experiments on the Reuters-21578 dataset and 20 Newsgroups dataset. This kernel consistently achieves better performance than the classical word kernel and word-sequence kernel on the two datasets. We also assessed the impact of word combination length on performance and compared the computing efficiency of this kernel to those of the word kernel and word-sequence kernel.},
	keywords = {Kernel methods, Machine learning, Support vector machine, Text classification, Word-combination kernel},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932\&partnerID=40\&md5=8649dfc8c1ee23ca70f2b137a74a19e4},
	author = {Zhang, L. and Hu, X. and Qin, S.}
}
