共查询到20条相似文献，搜索用时 703 毫秒
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在对知识流动相关文献进行梳理的基础上,对流体力学中的液体流动进行隐喻分析,研究知识流动与液体流动的对应关系。提出构建沿程损失模型的前提假设,应用流体力学圆管流动理论,构建基于流体力学的知识流动过程中的知识损失分析模型,对模型进行推导和求解。在此基础上,通过编程对知识流动沿程损失模型进行仿真模拟,绘制知识流动损失的变化趋势图像。通过研究发现知识流动过程中可以通过提高知识接收方自身知识接收上限及提高知识输出方的输出意愿、依靠科学决策方法等途径有效避免知识流动过程中产生不必要的损失,为提高知识传输效率提供了有益的借鉴。 相似文献
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水平井开发底水油藏具有明显的技术优势,国内外学者对底水油藏水平井的开发机理进行了大量的物理模拟研究。通过对已有文献的深入分析发现,水平井的水脊上升形态和水平井产能受到生产制度和井筒模型的影响,在与现场原型相似的物理模型中开展的实验结果才能够代表现场实际规律。在常用的油藏渗流相似准则数的基础上,本文提出以水平井的跟趾端压降与生产压差之比作为井筒与油藏耦合相似的基础,并结合几何相似提出了一套底水油藏水平井物理模拟实验相似化设计方法。根据设计方法上编制了相关计算程序,通过数值计算方法实现了相关参数的非线性求解。 相似文献
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战略类型对创新选择的影响:基于能力的观点 总被引：2，自引：0，他引：2
基于M-S理论和能力观点,将能力开发策略概念引入到战略类型与创新选择实现之间关系的理论分析框架中,并在理论分析的基础上得到了8个命题.分析结果表明,探索型战略企业通过提升能力探索完成突变创新,防御型战略企业通过应用现有能力实现渐进创新绩效的提高,分析型战略企业同时促进能力应用和能力探索两种能力开发,保证了突变创新和渐进创新同时有效地开展,获得短期利润并维持了企业长期竞争优势. 相似文献
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本文在前人工作的基础上,较为系统地介绍了歧离带变化率法,然后在理论和模型实验两方面对其进行了较为深入的研究,并编写出了球体的理论曲线程序.最后,通过野外实例说明了歧变法在联剖异常解释中的应用效果.理论计算、模型实验及应用实例的结果都表明:在一定条件下对联剖曲线做歧变法处理,往往会收到比较好的地质效果. 相似文献
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基于灰色模糊综合评价的干部考核优化方法 总被引：6，自引：0，他引：6
本文利用灰色理论、模糊数学理论和决策支持系统理论，在以往的基础上改进并建立了用于干部考核的灰色模糊综合评价模型，给出了相应电子政务中计算机实现方法．并结合作者的开发经验，通过某高校党委组织部干部考核应用实例，证明了这一模型和方法的科学性和有效性。 相似文献
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In this paper we present novel ensemble classifier architectures and investigate their influence for offline cursive character recognition. Cursive characters are represented by feature sets that portray different aspects of character images for recognition purposes. The recognition accuracy can be improved by training ensemble of classifiers on the feature sets. Given the feature sets and the base classifiers, we have developed multiple ensemble classifier compositions under four architectures. The first three architectures are based on the use of multiple feature sets whereas the fourth architecture is based on the use of a unique feature set. Type-1 architecture is composed of homogeneous base classifiers and Type-2 architecture is constructed using heterogeneous base classifiers. Type-3 architecture is based on hierarchical fusion of decisions. In Type-4 architecture a unique feature set is learned by a set of homogeneous base classifiers with different learning parameters. The experimental results demonstrate that the recognition accuracy achieved using the proposed ensemble classifier (with best composition of base classifiers and feature sets) is better than the existing recognition accuracies for offline cursive character recognition. 相似文献
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A proposed particle swarm classifier has been integrated with the concept of intelligently controlling the search process of PSO to develop an efficient swarm intelligence based classifier, which is called intelligent particle swarm classifier (IPS-classifier). This classifier is described to find the decision hyperplanes to classify patterns of different classes in the feature space. An intelligent fuzzy controller is designed to improve the performance and efficiency of the proposed classifier by adapting three important parameters of PSO (inertia weight, cognitive parameter and social parameter). Three pattern recognition problems with different feature vector dimensions are used to demonstrate the effectiveness of the introduced classifier: Iris data classification, Wine data classification and radar targets classification from backscattered signals. The experimental results show that the performance of the IPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) classifiers, which are two conventional classifiers. 相似文献
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《Information processing & management》2019,56(4):1300-1316
Dynamic Ensemble Selection (DES) strategy is one of the most common and effective techniques in machine learning to deal with classification problems. DES systems aim to construct an ensemble consisting of the most appropriate classifiers selected from the candidate classifier pool according to the competence level of the individual classifier. Since several classifiers are selected, their combination becomes crucial. However, most of current DES approaches focus on the combination of the selected classifiers while ignoring the local information surrounding the query sample needed to be classified. In order to boost the performance of DES-based classification systems, we in this paper propose a dynamic weighting framework for the classifier fusion during obtaining the final output of an DES system. In particular, the proposed method first employs a DES approach to obtain a group of classifiers for a query sample. Then, the hypothesis vector of the selected ensemble is obtained based on the analysis of consensus. Finally, a distance-based weighting scheme is developed to adjust the hypothesis vector depending on the closeness of the query sample to each class. The proposed method is tested on 30 real-world datasets with six well-known DES approaches based on both homogeneous and heterogeneous ensemble. The obtained results, supported by proper statistical tests, show that our method outperforms, both in terms of accuracy and kappa measures, the original DES framework. 相似文献
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基于CBERS-1图像的干旱半干旱区土地利用分类 总被引：5，自引：0，他引：5
以中巴资源卫星CBERS 1图像数据为信息源,分别采用最大似然法、BP神经网络和Fuzzy ARTMAP神经网络 3种分类器,以位于干旱区的中国新疆石河子地区为例,进行了土地利用计算机自动分类。结果认为,3种方法中以Fuzzy ARTMAP神经网络法分类精度最高,分别比最大似然法和BP神经网络法提高了 1 0.69%和 6.84%。同时也证实了CBERS 1图像在土地利用调查中的实用性 相似文献
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Arkaitz Zubiaga Elena Kochkina Maria Liakata Rob Procter Michal Lukasik Kalina Bontcheva Trevor Cohn Isabelle Augenstein 《Information processing & management》2018,54(2):273-290
Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest to researchers. While most previous work has focused on using individual tweets as classifier inputs, here we report on the performance of sequential classifiers that exploit the discourse features inherent in social media interactions or ‘conversational threads’. Testing the effectiveness of four sequential classifiers – Hawkes Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM) – on eight datasets associated with breaking news stories, and looking at different types of local and contextual features, our work sheds new light on the development of accurate stance classifiers. We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers. Furthermore, we show that LSTM using a reduced set of features can outperform the other sequential classifiers; this performance is consistent across datasets and across types of stances. To conclude, our work also analyses the different features under study, identifying those that best help characterise and distinguish between stances, such as supporting tweets being more likely to be accompanied by evidence than denying tweets. We also set forth a number of directions for future research. 相似文献
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In practical text classification tasks, the ability to interpret the classification result is as important as the ability to classify exactly. Associative classifiers have many favorable characteristics such as rapid training, good classification accuracy, and excellent interpretation. However, associative classifiers also have some obstacles to overcome when they are applied in the area of text classification. The target text collection generally has a very high dimension, thus the training process might take a very long time. We propose a feature selection based on the mutual information between the word and class variables to reduce the space dimension of the associative classifiers. In addition, the training process of the associative classifier produces a huge amount of classification rules, which makes the prediction with a new document ineffective. We resolve this by introducing a new efficient method for storing and pruning classification rules. This method can also be used when predicting a test document. Experimental results using the 20-newsgroups dataset show many benefits of the associative classification in both training and predicting when applied to a real world problem. 相似文献
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In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two well-known classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is that we employ the improved Rocchio model to speed up the categorization process of KNN. Extensive experiments conducted on both English and Chinese corpora show that GCCC and its variants have better categorization ability than some state-of-the-art classifiers, i.e., Rocchio, KNN and Support Vector Machine (SVM). 相似文献
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《Information processing & management》2020,57(2):102180
Search task success rate is an important indicator to measure the performance of search engines. In contrast to most of the previous approaches that rely on labeled search tasks provided by users or third-party editors, this paper attempts to improve the performance of search task success evaluation by exploiting unlabeled search tasks that are existing in search logs as well as a small amount of labeled ones. Concretely, the Multi-view Active Semi-Supervised Search task Success Evaluation (MA4SE) approach is proposed, which exploits labeled data and unlabeled data by integrating the advantages of both semi-supervised learning and active learning with the multi-view mechanism. In the semi-supervised learning part of MA4SE, we employ a multi-view semi-supervised learning approach that utilizes different parameter configurations to achieve the disagreement between base classifiers. The base classifiers are trained separately from the pre-defined action and time views. In the active learning part of MA4SE, each classifier received from semi-supervised learning is applied to unlabeled search tasks, and the search tasks that need to be manually annotated are selected based on both the degree of disagreement between base classifiers and a regional density measurement. We evaluate the proposed approach on open datasets with two different definitions of search tasks success. The experimental results show that MA4SE outperforms the state-of-the-art semi-supervised search task success evaluation approach. 相似文献
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以我国制造业上市公司为样本数据,用支持向量机作为基分类器的集成学习方法来预测企业的财务危机,通过具体实验分析可知:集成学习比单个基分类器的预测准确率提高了4个百分点,且稳定性更高,有效地提高了模型的预测精度,使得模型更具有准确性和应用性。基于支持向量机的集成学习方法在构建我国制造业上市公司财务危机预警模型上是有效的,且达到一定的财务危机预警效果。 相似文献