检索结果-内蒙古大学图书馆

Impact of IT Capability on Inventory management: An Empirical Study 8

Impact of IT Capability on Inventory Management: An Empirica...

8th International Conference on Information Technology and Quantitative management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Tian, Xin Wang, Haoqing Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Despite the widespread adoption of technology in inventory management, empirical research that examines the effect of these technologies is scarce. Does IT capability really have a positive effect on the inventory management? This paper empirically studies the usage of information technology by the definition of IT capability. Our unique panel data set allows us to explore the relationship between technology usage and inventory management. Our results show that IT capability has a positive effect on the inventory strategy and inventory operation process, and has a negative effect on the out-of-stock level. Our research demonstrates the superiority of the usage of technology. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： intelligent warehousing business intelligence inventory management logistics

来源：评论

学校读者我要写书评

暂无评论

Search Engine Marketing, Financing Ability and Firm Performance in E-commerce 3

Search Engine Marketing, Financing Ability and Firm Performa...

引用

3rd International Conference on Information Technology and Quantitative management (ITQM)

作者： Yang, Zhuofan Shi, Yong Wang, Bo Univ Chinese Acad Sci Management Sch Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Res Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

This paper explores the relationship of search engine marketing, financing ability and e-commerce firm performance by the empirical research on China's B2C e-commerce firms. Results show that search engine marketing and business model has a strong positive relation with firm performance while financing ability has a negative effect on firm performance. It verifies the low returns to inputs in e-commerce and enlightens the managers should concentrate on business model innovation and consumer relation management. (C) 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Search Engine Marketing E-commerce Financing ability Firm performance

来源：评论

学校读者我要写书评

暂无评论

A Model-Embedded Trend Test with Incorporating Hardy-Weinberg Equilibrium Information

引用

Journal of Systems Science & Complexity 2017年第1期30卷 101-110页

作者： HU Xiaonan DUAN Xiaogang PAN Dongdong ZHANG Sanguo LI Qizhai School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China KeyLaboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190China. School of Statistics Beijing Normal University Beijing 100875 China. Department of Statistics Yunnan University Kunming 650091 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy ofSciences Beijing 100190 China.

The genetic models are greatly important in the analysis of genetic epidemiologic studies and many of the studies are conducted using the trend test under the additive model. However, for many complex diseases and traits, the underlying genetic model for a genetic locus is usually uncertain. So a robust test free of genetic model is appropriate. In this paper, the authors propose a model-embedded trend test by incorporating Hardy-Weinberg equilibrium information and obtain the explicit formula to calculate its statistical significance. Extensive simulation studies show the proposed test is more robust than the existing procedures. Finally, a real application is further analyzed to show the performance of the proposed test.

关键词： Genetic model hardy-weinberg equilibrium power robust trend test.

来源：评论

学校读者我要写书评

暂无评论

Cross-media Topic Detection with Refined CNN based Image-Dominant Topic Model 15

Cross-media Topic Detection with Refined CNN based Image-Dom...

引用

ACM International Conference on Multimedia (ACM Multimedia)

作者： Wang, Zhiyi Li, Liang Huang, Qingming Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781450334594

Online heterogenous data is springing up while the data has the rich auxiliary information (e.g. pictures and videos) around the text. However, traditional topic models are suffering from the limitations to discover the topics effectively from the cross-media data. Incorporating with the convolutional neural network (CNN) feature, we propose a novel image dominant topic model, which projects both the text modality and the visual modality into a semantic simplex. Further, an improved CNN feature is introduced to capture more visual details by fusing the convolutional layer and fully-connected layer. Experimental comparisons with state-of-the-art methods in the cross-media topic detection task show the effectiveness of our model.

关键词： Convolutional Neural Networks Topic Model Cross-media topic Detection

来源：评论

学校读者我要写书评

暂无评论

Forecasting China's Service Outsourcing Development with an EMD-VAR-SVR Ensemble Method

Forecasting China's Service Outsourcing Development with an ...

引用

4th International Conference on Information Technology and Quantitative management (ITQM) - Promoting Business Analytics and Quantitative management of Technology

作者： Yi, Siqi Guo, Kun Chen, Zhensong Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big DataMin & Knowledge Management Beijing 100190 Peoples R China

Service outsourcing is playing a more and more important role in the world economy. With the fast development of information technology and service industry, China is witnessing an accelerated growth momentum of service outsourcing. In the first half year of 2015, the total service-outsourcing executive contracts value of China is increased by 9.7% to US $40.8 billion. Looking ahead 10 years, China's service outsourcing market will receive unprecedented opportunities and challenges, predicting the contract amount of service outsourcing can promote the healthy development of the industry. In this paper, an EMD-VAR-SVR ensemble method is proposed for predicting the executive amount of servicing outsourcing. In order to verify and test the forecasting capability of the proposed EMD-VAR-SVR ensemble method, the single VAR model and single SVR model are used as the benchmark models. The empirical result shows that the proposed ensemble method's prediction has the best effect. (C) 2016 Published by Elsevier B.V.

关键词： Service outsourcing Empirical mode decomposition (EMD) Vector auto-regression (VAR) Support vector regression (SVR) Forecasting

来源：评论

学校读者我要写书评

暂无评论

Extreme Market Prediction for Trading Signal with Deep Recurrent Neural Network 18th

Extreme Market Prediction for Trading Signal with Deep Recur...

引用

18th International Conference on Computational Science (ICCS)

作者： Lu, Zhichen Long, Wen Guo, Ying Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9783319937014;9783319937007

Recurrent neural network are a type of deep learning units that are well studied to extract features from sequential samples. They have been extensively applied in forecasting univariate financial time series, however their application to high frequency multivariate sequences has been merely considered. This paper solves a classification problem in which recurrent units are extended to deep architecture to extract features from multi-variance market data in 1-minutes frequency and extreme market are subsequently predicted for trading signals. Our results demonstrate the abilities of deep recurrent architecture to capture the relationship between the historical behavior and future movement of high frequency samples. The deep RNN is compared with other models, including SVM, random forest, logistic regression, using CSI300 1-minutes data over the test period. The result demonstrates that the capability of deep RNN generating trading signal based on extreme movement prediction support more efficient market decision making and enhance the profitability.

关键词： Recurrent neural networks Deep learning High frequency trading Financial time series

来源：评论

学校读者我要写书评

暂无评论

Constrained matrix factorization for semi-weakly learning with label proportions

引用

PATTERN RECOGNITION 2019年 91卷 13-24页

作者： Chen, Zhensong Shi, Yong Qi, Zhiquan Capital Univ Econ & Business Informat Sch Beijing 100070 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Learning with label proportions (LLP) is a weakly supervised learning problem that is conceivable in many real-world applications, where the training data is given in bags of instances, and only knowing the proportions of data points belonging to a particular category for each bag. However, how to effectively address the LLP problem with high dimensional data and some labeled samples is still a challenging problem. In this paper, we firstly propose a novel learning problem called semi-weakly learning with label proportions (SLLP), which has more extensive application scenarios. Then, we contribute a novel method based on non-negative matrix factorization, called Proportion Constrained Matrix Factorization (PCMF). It can not only effectively incorporate the label and proportion information, but also explore the local manifold structure information of training data. Moreover, the proposed method can make the data points from the same class be more likely merged together in the latent representation space, which leads to the more discriminating power. Sufficient experimental results on the benchmark datasets demonstrates its superiority over the state-of-the-art methods for LLP problem and efficiency on solving the SLLP problem. (C) 2019 Published by Elsevier Ltd.

关键词： Matrix factorization Semi-weakly learning with label proportions Weakly supervised Dimension reduction Local geometric consistency

来源：评论

学校读者我要写书评

暂无评论

Algorithm-Dependent Generalization of AUPRC Optimization: Theory and Algorithm

引用

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第7期46卷 5062-5079页

作者： Wen, Peisong Xu, Qianqian Yang, Zhiyong He, Yuan Huang, Qingming Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 101408 Peoples R China Alibaba Grp Hangzhou Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management BDKM Beijing 101408 Peoples R China

Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning. Despite extensive studies on AUPRC optimization, generalization is still an open problem. In this work, we present the first trial in the algorithm-dependent generalization of stochastic AUPRC optimization. The obstacles to our destination are three-fold. First, according to the consistency analysis, the majority of existing stochastic estimators are biased with biased sampling strategies. To address this issue, we propose a stochastic estimator with sampling-rate-invariant consistency and reduce the consistency error by estimating the full-batch scores with score memory. Second, standard techniques for algorithm-dependent generalization analysis cannot be directly applied to listwise losses. To fill this gap, we extend the model stability from instance-wise losses to listwise losses. Third, AUPRC optimization involves a compositional optimization problem, which brings complicated computations. In this work, we propose to reduce the computational complexity by matrix spectral decomposition. Based on these techniques, we derive the first algorithm-dependent generalization bound for AUPRC optimization. Motivated by theoretical results, we propose a generalization-induced learning framework, which improves the AUPRC generalization by equivalently increasing the batch size and the number of valid training examples. Practically, experiments on image retrieval and long-tailed classification speak to the effectiveness and soundness of our framework.

关键词： Optimization Stability analysis Stochastic processes Measurement Standards Approximation algorithms Machine learning algorithms Machine learning AUPRC learning to rank algorithm-dependent generalization stability

来源：评论

学校读者我要写书评

暂无评论

Online learning affinity measure with CovBoost for multi-target tracking

引用

NEUROCOMPUTING 2015年 168卷 327-335页

作者： Li, Guorong Huang, Qingming Jiang, Shuqiang Xu, Yingkun Zhang, Weigang Univ Chinese Acad Sci CAS Beijing 100190 Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing 100080 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Harbin Inst Tech Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China

In this paper, we propose a new online learning method for measuring affinity between tracklets in multi-target tracking. As targets and background usually keep changing in the video, fixed affinity measurement could not adapt to their variations. Most existing affinity learning methods construct labeled samples based on the obtained tracklets, and then minimize a predefined loss function to get an optimal affinity measurement. However, those methods simply assume that the training error equals to testing error which is not true in many of real time tracking scenarios. Differently, we propose to learn affinity measurement through CovBoosting, which considers the evolution of the tracklets and could obtain affinity measurement with more discriminative ability. To deal with targets' disappearance and new targets' appearance, we combine tracklet affinity with contextual information to do an optimal inference. Moreover, an online updating algorithm is developed to guarantee that the learned tracklet affinity is always optimal for tracking targets in current sliding window. Experimental results on benchmark datasets demonstrate that tracklet affinity learned with our method is more discriminative and could greatly improve the performance of the multi-target tracker. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-target tracking Tracklet affinity CovBoost Online learning

来源：评论

学校读者我要写书评

暂无评论

ν-Structural Nonparallel Support Vector Machine for Pattern Classification

ν-Structural Nonparallel Support Vector Machine for Pattern...

引用

IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Chen, Dandan Tian, Yingjie Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Sch Math Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9781509047710

The nu-nonparallel support vector machine (nu-SNPSVM) for classification has the advantage of using a parameter. on controlling the number of support vectors. However, it ignores the prior structural information in data. In this paper, we propose a novel nonparallel classifier, named nu-Structural Nonparallel Support Vector Machine (nu-SNPSVM), for binary classification. Each model of nu-SNPSVM considers not only the compactness in both classes by the structural information but also the separability between classes, thus it can fully exploit prior knowledge to directly improve the algorithm's generalization capacity and is certainly superior to NPSVMs and other twin SVMs theoretically. Experimental results on lots of data sets show the effectiveness of our method.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：