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11.
介绍本体学习的概念和发展,分析现有的本体学习的体系结构,研究中文本体学习这一领域存在的主要问题,包括中文语料特有的分词、词性和行文灵活问题等,基于中文本体获取中存在的困难,针对中文语料中丰富的句型要素,提出基于句型规则的自举本体学习方法,构建整个方法的框架模型,详细分析该模型以及其运行流程中的两个关键环节：规则学习环节和本体学习环节。最后分析该方法的特点和意义。 相似文献
12.
OTL电路的教学设计 总被引：1，自引：0，他引：1
OTL电路是《模拟电子技术》课程的教学重点和难点。其教学过程应充分利用学生已有的知识,采用问题解的方法,逐渐完善电路,力争重现科学的演进过程,进一步提高学生的电路分析能力和设计能力。 相似文献
13.
文章首先将复杂网络研究方法引入到舆情网络信息分析之中,并介绍了该方法的基本研究步骤;重点介绍了目前比较成熟且与复杂网络结合紧密的网络病毒传播理论和舆情网络模型;最后,文章提出了基于复杂网络的舆情信息治理的具体办法,包括3种免疫策略和靴襻渗流模型策略。文章为搭建高效合理的舆情网络环境提供了一种研究新思路,进而可以帮助政府提升对网络舆情信息管理的水平。 相似文献
14.
中国新能源汽车产业补贴政策初衷是激励企业通过研发创新形成全球竞争力，结果却短期内促进了产业规模的快速提升，企业研发投入积极性反而严重不足。现有研究认识到企业行为会影响政策传导效用，但未从政策作用点与传导路径视角深入剖析政策对于企业行为的影响。本文综合运用内容分析法与Bootstrap中介效应模型，将文本分析与实证有机结合到一起，纳入企业规模与企业专利行为作为中介变量，分两条路径对二者的不同作用进行了对比分析。实证研究结果表明：政府支持显著促进了企业绩效、规模与专利产出的提升，且企业规模与专利行为在政策对绩效影响中存在显著的中介作用，其中企业规模的中介效用强于专利行为。新能源汽车双积分等新补贴政策应综合考虑制度、补贴工具及产业特征等因素影响，避免政策在“政府-企业界面”产生偏移。 相似文献
15.
Xijuan Zhang 《Structural equation modeling》2016,23(3):392-408
Bootstrapping approximate fit indexes in structural equation modeling (SEM) is of great importance because most fit indexes do not have tractable analytic distributions. Model-based bootstrap, which has been proposed to obtain the distribution of the model chi-square statistic under the null hypothesis (Bollen & Stine, 1992), is not theoretically appropriate for obtaining confidence intervals (CIs) for fit indexes because it assumes the null is exactly true. On the other hand, naive bootstrap is not expected to work well for those fit indexes that are based on the chi-square statistic, such as the root mean square error of approximation (RMSEA) and the comparative fit index (CFI), because sample noncentrality is a biased estimate of the population noncentrality. In this article we argue that a recently proposed bootstrap approach due to Yuan, Hayashi, and Yanagihara (YHY; 2007) is ideal for bootstrapping fit indexes that are based on the chi-square. This method transforms the data so that the “parent” population has the population noncentrality parameter equal to the estimated noncentrality in the original sample. We conducted a simulation study to evaluate the performance of the YHY bootstrap and the naive bootstrap for 4 indexes: RMSEA, CFI, goodness-of-fit index (GFI), and standardized root mean square residual (SRMR). We found that for RMSEA and CFI, the CIs under the YHY bootstrap had relatively good coverage rates for all conditions, whereas the CIs under the naive bootstrap had very low coverage rates when the fitted model had large degrees of freedom. However, for GFI and SRMR, the CIs under both bootstrap methods had poor coverage rates in most conditions. 相似文献
16.
Haiyan Bai Stephen A. Sivo Wei Pan Xitao Fan 《International Journal of Research & Method in Education》2016,39(2):194-207
Among the commonly used resampling methods of dealing with small-sample problems, the bootstrap enjoys the widest applications because it often outperforms its counterparts. However, the bootstrap still has limitations when its operations are contemplated. Therefore, the purpose of this study is to examine an alternative, new resampling method (called S-SMART) and compare the statistical performance of it with that of the bootstrap through an application of them to the most advanced modelling technique, SEM, as an example. The evaluation of the statistical performances of S-SMART and the bootstrap with respect to the standard errors of the parameter estimates was conducted through a Monte Carlo simulation study. This work, while potentially benefiting educational and behavioural research, conceivably would also provide methodological support for other research areas, such as bioinformatics, biology, geosciences, astronomy, and ecology, where large samples are hard to obtain. 相似文献
17.
Fit indexes are an important tool in the evaluation of model fit in structural equation modeling (SEM). Currently, the newest confidence interval (CI) for fit indexes proposed by Zhang and Savalei (2016) is based on the quantiles of a bootstrap sampling distribution at a single level of misspecification. This method, despite a great improvement over naive and model-based bootstrap methods, still suffers from unsatisfactory coverage. In this work, we propose a new method of constructing bootstrap CIs for various fit indexes. This method directly inverts a bootstrap test and produces a CI that involves levels of misspecification that would not be rejected in a bootstrap test. Similar in rationale to a parametric CI of root mean square error of approximation (RMSEA) based on a noncentral χ2 distribution and a profile-likelihood CI of model parameters, this approach is shown to have better performance than the approach of Zhang and Savalei (2016), with more accurate coverage and more efficient widths. 相似文献
18.
This Monte Carlo simulation study investigated the impact of nonnormality on estimating and testing mediated effects with the parallel process latent growth model and 3 popular methods for testing the mediated effect (i.e., Sobel’s test, the asymmetric confidence limits, and the bias-corrected bootstrap). It was found that nonnormality had little effect on the estimates of the mediated effect, standard errors, empirical Type I error, and power rates in most conditions. In terms of empirical Type I error and power rates, the bias-corrected bootstrap performed best. Sobel’s test produced very conservative Type I error rates when the estimated mediated effect and standard error had a relationship, but when the relationship was weak or did not exist, the Type I error was closer to the nominal .05 value. 相似文献
19.
During the early phases of research, semiparametric models (SPMs) have the advantage of recovering latent nonlinearity over parametric counterparts. Structural equation mixture models (Bauer, 2005) can be applied as SPMs to flexibly recover and describe the form of the unknown latent relationship with minimal distributional assumptions. This short report extends the work on this SPM (Bauer, 2005; Pek, Losardo & Bauer, 2011) by developing approximate simultaneous confidence bands or confidence envelopes (CEs) to evaluate potential nonlinearity of the unknown latent function. A line-finding algorithm to be used in conjunction with these CEs is also developed as an implementation of an informal test to diagnose nonlinearity. Coverage of the CEs and performance of the algorithm in terms of rates of detecting latent nonlinearity are evaluated by Monte Carlo. Recommendations for the use of these CEs and the algorithm for detecting nonlinearity are suggested. 相似文献
20.
Holmes Finch 《Journal of Experimental Education》2013,81(1):26-52
Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by interpreting the contributions of the individual predictors to this linear combination, often using structure coefficients (SC). The authors of this simulation study examine the utility of several methods for interpreting SCs. Results indicate that with samples greater than 100, a bootstrap confidence interval may be optimal, whereas with smaller samples, common rules of thumb may work best. Furthermore, nonnormal data and unequal covariance matrixes diminish the effectiveness of SCs as an interpretive tool. 相似文献