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利用英文全文数据库利用英文全文数据库——Elsevier 进行文献信息检索示例1、检索课题名称：我国新农村沼气建设2、课题分析：中文关键词为：我国新农村，沼气，建设英文关键词为：country，biogas，development3、选择检索工具：Elsevier 数据库4、构建检索策略： country AND biogas AND development5、简述检索过程:选定在Elsevier 中期刊、图书、文摘数据库等全部文献资源中检索 1996 年以后的关于我国新农村沼气建设的相关文献。
利用确定的检索策略（waste water AND treatment），文献全文（含文献题目、摘要、关键词）中检索，检到 9735篇相关文献；在文献题目、摘要和关键词中检索，检索到984 篇相关文献；在文献关键词中检索到 137 篇相关文献；在文献题目中检索到 114篇相关文献。
6、整理检索结果：从以上文献中选择出2 条切题文献1.Biogas, renewable energy resource for PakistanSyed S. Amjid , , Muhammad Q. Bilal, Muhammad S. Nazir, Altaf Hussain Department of Livestock Management, University of Agriculture, Faisalabad, Pakistan Received 31 January Mechanical characteristics evaluation of biogas micro turbine power systems 2011; Accepted 20 February 2011. Available online 5 May 2011.2.Kwang-beom Hur , , Sang-kyu Rhim, Jung-keuk ParkKorea Electric Power Corporation (KEPCO), Korea Electric Power Research Institute (KEPRI), 103-16 Munji-dong, Yusung-gu, Daejeon 305-380, Republic of KoreaReceived 12 September 2007; revised 6 August 2009; Accepted 14 August 2009. Available online 20 August 2009.7、全文摘录选择一篇：Biogas, renewable energy resource for Pakistan一、篇名：Biogas, renewable energy resource for Pakistan二、著者：Syed S. Amjid , , Muhammad Q. Bilal, Muhammad S. Nazir, Altaf Hussain三、著者机构：Department of Livestock Management, University of Agriculture, Faisalabad, PakistanReceived 31 January 2011; Accepted 20 February 2011. Available online 5 May 2011。
（四）利用英文全文数据库——Elsevier 进行文献信息检索示例1、检索课题名称：现代建筑结构体系研究2、课题分析：“现代建筑”属于本课题中的主体，其应用目标是“结构体系”的研究，而“研究”是句法修饰，故“研究”可不作为检索词，由此得出如下检索词（按其对课题影响程度排序）：中文关键词为：1 现代建筑 2 结构体系英文关键词为：（1）Model architacture（2）Structure system3、选择检索工具：Elsevier 数据库4、构建检索策略：Model architacture Structure system5、简述检索过程:选定在 Elsevier 中期刊、图书、文摘数据库等全部文献资源中检索1996 年以后的关于现代建筑结构体系研究的相关文献。
利用确定的检索策略（Model architacture Structure system），文献全文（含文献题目、摘要、关键词）中检索，检到 8728 篇相关文献；在文献题目、摘要和关键词中检索，检索到 1212 篇相关文献；在文献关键词中检索到 134 篇相关文献；在文献题目中检索到 178 篇相关文献。
6、整理检索结果：从以上文献中选择出3 条切题文1 Component-based discriminative classification for hidden Markov models Original Research ArticlePattern Recognition, Volume 42, Issue 11, November 2009, Pages 2637-2648Manuele Bicego, Elżbieta Pe¸kalska, David M.J. Tax, Robert P.W. Duin| PDF (316 K)| Related articles| Related reference work articles2 Chapter3 Psychosocial Value of architacture Simulationfor Extended Spaceflight Original Research ArticleAdvances in architacture Biology and Medicine, Volume6, 1997, Pages 81-91Nick Kanas| Related articles | Related reference work articles3 Multidimensional Data Structures: Review and Outlook Original Research Article Advances in Computers, Volume 27, 1988, Pages 69-119S. Sitharama Iyengar, N.S.V. Rao, R.L. Kashyap, V.K. Vaishnavi| Related articles | Related reference work articles6、全文摘录选择一篇：Component-based discriminativeclassification for hidden Markov models一、篇名Component-based discriminative classification forhidden Markov models∙二、著者Manuele Bicego∙三、著者机构Elżbieta Pe¸kalska c,∙David M.J. Tax d,∙Robert P.W. Duin d∙四、文摘Hidden Markov models (HMMs) have been successfully applied to a wide range of sequence modeling problems. In the classification context, one of the simplest approaches is to train a single HMM per class. A test sequence is then assigned to the class whose HMM yields the maximum a posterior (MAP) probability. This generative scenario works well when the models are correctly estimated. However, the results can become poor when improper models are employed, due to the lack of prior knowledge, poor estimates, violated assumptions or insufficient training data.To improve the results in these cases we propose to combine the descriptive strengths of HMMs with discriminative classifiers.This is achieved by training feature-based classifiers in an HMM-induced vector space defined by specific components of individual hidden Markov models.We introduce four major ways of building such vector spaces and study which trained combiners are useful in which context. Moreover, we motivate and discuss the merit of our method in comparison to dynamic kernels, in particular, to the Fisher Kernel approach.∙五、关键词Keywords: Hidden Markov models;∙Discriminative classification;∙Dimensionality reduction;∙Hybrid models;∙Generative embeddings六、正文Component-based discriminative classification for hidden Markov models（首段）The HMMs are fitted to model a single class well, but this may lead to poor discrimination as the models are not optimized to differentiate among the classes. We propose to derive a fixed-dimensional feature space from the trained generative HMMs, in which discriminative classifiers are trained. We call this an HMMVS, equipped with the traditional norm and Euclidean metric. Every feature is extracted from aspecific HMM and conveys information about the corresponding class. In essence, this approach maps variable-length observation sequences into a vector space, and by doing this it integrates the modeling potential of one-class models with discriminative classifiers.HMMVS are based on “Component Information” features, CI s, which describe some relevant information extracted from particular components of the models, in relation to the input sequence O. A CI feature either characterizes some properties of the generation path of the sequence O through the model λc or the strength with which a specific component of λc “responds” to O. More formally, FCI(·,λc):Oc→Rmc is a model-dependent mapping defined by mc components derived from λc. The final HMM-induced vector space is a Cartesian product of all CI-spaces (one for each class)（末段）This application aims at the examination of EEG signals in order to distinguish between alcoholic and control subjects, /databases/eeg. Each subject was exposed to either a single stimulus (S1) or two stimuli (S1 and S2) which were pictures of objects chosen from the 1980s Snodgrass and Vanderwart picture set. When two stimuli were shown, they were presented in either a matched condition whereS1 was identical to S2 or in a non-matched condition where S1 differed from S2. There are three different versions of the data. In our case, we use the Large Data Set, denoted here denote it as Alcoholic data, in which the training and test sets are already pre-defined. The training set contains data for 10 alcoholic and 10 control subjects, with 10 runs per subject per paradigm. This results in 600 training sequences. The test data use the same alcoholic and control subjects, but with 10 out-of-sample runs per subject per paradigm. This results in 600 test sequences.Each data set contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9-ms epoch) for 1 s. We select the first two channels only, as they permitted an almost perfect discrimination in the case of “small dataset”. All HMMs are trained with the same number of states七、参考文献Referenc1。
EBSCO（外文期刊数据库）使用介绍 •EBSCOhost 由EBSCO Publishing出版•覆盖的学科范围包括：生物科学、工商经济、咨询科技、通讯传播、工程、教育、艺术、医药学等•检索结果为文献的题录、文摘信息。
其中部分文献提供全文（PDF 格式（查看PDF格式的全文要下载并安装Adobe Acrobat Reader软件，图书馆主页提供下载）、HTML格式）访问方式•任何一台接入校园网的计算机均可通过Web方式检索•进入EBSCOhost界面后，首先需要选择数据库，然后选择检索方式 •EBSCOhost主要检索方式为基本检索（basic search）和高级检索（advanced search）两种如果选择在单个数据库内检索，还有参考文献检索（Cited References）、图像检索（Images）、刊名浏览（Publications）、叙词浏览（Subject Terms）、索引浏览（Indexes）、等辅助检索工具基本检索基本检索是在篇名、作者、主题词、文摘中进行检索可做如下扩展–全文检索（在文章全文中进行检索）–逻辑“与”运算（如有两个及两个以上的检索词，自动进行“与”—and 的运算）–检索相关词还可做如下限定–全文限定（有全文）–参考文献限定（有参考文献）–学术性期刊限定（专家评定的期刊）–出版日期限定（可选择一个时间段）–出版物名称限定（在特定出版物中进行检索）–出版物类型限定（期刊论文、报纸、图书、其它文献）–页数限定（文章最多不超过多少页）–带图像的文章（所有、PDF格式、文本附图限定检索结果。
可以在如下的选项中进行选择：有全文、有参考文献、专家评定的期刊、出版日期、在特定出版物中检索、出版物类型、文献最多不超过多少页、是否附图高级检索•高级检索可以检索所有的字段，可以使用布尔逻辑运算符确定检索词之间的关系。
检索结果比基本检索更为精确•还可以扩展检索范围和对检索结果做限定•可以保存检索策略、回顾检索历史、组配检索、进行电子通告服务尔逻辑算符确定检索词间的关系，点击执行检索。
万方NSTL外文文献数据库使用说明
（1）访问万方NSTL外文文献数据库：打开图书馆网站->数据库资源列表->点击万方外文文献数据库->万方数据资源系统->点击外文文献数据库，进入检索界面；
（2）选择检索字段，输入检索词；
（3）点击检索结果中的“详细摘要信息”；
（4）如果有原文请求需要，直接点击“向nstl请求全文”，即可进入原文索取页面；
（5）进入原文索取界面，选择原文投递方式；
（6）填写原文索取的详细信息，并提交；注：一定要输入本人正确的用于接收原文的电子邮件地址
（7）提交成功，24小时内通过填写的E—mail地址收取原文。
图示：。
