02118nas a2200193   4500008004100000245009600041210006900137260000900206300001400215490000700229520142900236653001901665653002701684653003001711653002701741653002801768100001101796856011701807       2013                        eng d00aA novel fault identifying method with supervised classification and unsupervised clustering0 anovel fault identifying method with supervised classification an  c2013  a184 - 1890 v113 aTo 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.10aFault identify10aSubtractive clustering10aSupervised classification10aSupport vector machine10aUnsupervised clustering1 aXu, T.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84880836927&partnerID=40&md5=9f7c84bdac0bd7c607dac64aa37c736201802nas a2200205   4500008004100000245009300041210006900134260000900203300001400212490000700226520107700233653003301310653003201343653003401375653002701409100001501436700001601451700001201467856011701479       2013                        eng d00aOptimization and application of support vector machine based on SVM algorithm parameters0 aOptimization and application of support vector machine based on   c2013  a165 - 1690 v113 aThe 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.10aCustomer data classification10aKernel parameters selection10aParticle swarm pattern search10aSupport vector machine1 aYan, H.-F.1 aWang, W.-F.1 aLiu, J.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879098093&partnerID=40&md5=aa26078b417c2386e1244d36d3bacb0901371nas a2200205   4500008004100000245008000041210006900121260000900190300001400199490000700213520067000220653001500890653002700905653002800932653003400960653002700994100001201021700001501033856011701048       2012                        eng d00aExperimental evaluation for impact factors of online signature verification0 aExperimental evaluation for impact factors of online signature v  c2012  a187 - 1930 v103 aA 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.10aBiometrics10aDynamic timing warping10aExperimental evaluation10aOnline signature verification10aSupport vector machine1 aHao, C.1 aMing, D.D.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866459887&partnerID=40&md5=efb57c0a5eb8df586d615ad81371552d01966nas a2200217   4500008004100000245005200041210005200093260000900145300001400154490000700168520130000175653001901475653002101494653002701515653002401542653002801566100001401594700001101608700001201619856011701631       2012                        eng d00aWord combination kernel for text categorization0 aWord combination kernel for text categorization  c2012  a202 - 2110 v103 aWe 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.10aKernel methods10aMachine learning10aSupport vector machine10aText classification10aWord-combination kernel1 aZhang, L.1 aHu, X.1 aQin, S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866439932&partnerID=40&md5=8649dfc8c1ee23ca70f2b137a74a19e4