检索结果-内蒙古大学图书馆

Trading strategy based on dynamic mode decomposition: Tested in Chinese stock market

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2016年第0期461卷 498-508页

作者： Cui, Ling-xiao Long, Wen 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

Dynamic mode decomposition (DMD) is an effective method to capture the intrinsic dynamical modes of complex system. In this work, we adopt DMD method to discover the evolutionary patterns in stock market and apply it to Chinese A-share stock market. We design two strategies based on DMD algorithm. The strategy which considers only timing problem can make reliable profits in a choppy market with no prominent trend while fails to beat the benchmark moving-average strategy in bull market. After considering the spatial information from spatial-temporal coherent structure of DMD modes, we improved the trading strategy remarkably. Then the DMD strategies profitability is quantitatively evaluated by performing SPA test to correct the data-snooping effect. The results further prove that DMD algorithm can model the market patterns well in sideways market. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Dynamic mode decomposition Technical analysis Trading strategy Superior predictive ability

来源：评论

学校读者我要写书评

暂无评论

Cooperation in Two-Stage Games on Undirected Networks

引用

Journal of Systems Science & Complexity 2017年第3期30卷 680-693页

作者： GAO Hongwei PETROSYAN Leon QIAO Han SEDAKOV Artem School of Mathematics and Statistics Qingdao UniversityQingdao 266071China. Faculty of Applied Mathematics and Control Processes Saint Petersburg State UniversitySaint Petersburg 198504Russia School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China

In the paper, cooperative two-stage network games are studied. At the first stage of the game, players form a network, while at the second stage players choose their behaviors according to the network realized at the first stage. As a cooperative solution concept in the game, the core is *** is proved that some imputations from the core are time inconsistent, whereas one can design for them a time-consistent imputation distribution procedure. Moreover, the strong time consistency problem is also investigated.

关键词： Characteristic function cooperation core imputation imputation distribution proce-dure network strong time consistency T-value.

来源：评论

学校读者我要写书评

暂无评论

When Ensemble Learning Meets Deep Learning: a New Deep Support Vector Machine for Classification

引用

knowledge-BASED SYSTEMS 2016年 107卷 54-60页

作者： Qi, Zhiquan Wang, Bo Tian, Yingjie Zhang, Peng 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 Univ Technol Sydney Sydney NSW 2007 Australia

Recently, Deep Learning (DL) method has received a significant breakthrough in the data representation, whose success mainly depends on its deep structure. In this paper, we focus on the DL research based on Support Vector Machine (SVM), and first present an Ex-Adaboost learning strategy, and then propose a new Deep Support Vector Machine (called DeepSVM). Unlike other DL algorithms based on SVM, in each layer, Ex-Adaboost is applied to not only select SVMs with the minimal error rate and the highest diversity, but also to produce the weight for each feature. In this way, new training data is obtained. By stacking these SVMs into multiple layers following the same way, we finally acquire a new set of deep features that can greatly boost the classification performance. In the end, the training data represented by these new features is regarded as the input for a standard SVM classifier. In the experimental part, we offer these answers to the following questions: 1) is the deep structure of DeepSVM really useful for classification problem? 2) Does Ex-Adaboost work, and is it helpful for further improving on DeepSVM's performance with respect to the deep structure? 3) How much improvement in classification accuracy of DeepSVM, compared with other exist algorithms? (C) 2016 Elsevier B.V. All rights reserved.

关键词： Pattern recognition Deep architectures Support vector machine

来源：评论

学校读者我要写书评

暂无评论

Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning

Geological Disaster Recognition on Optical Remote Sensing Im...

引用

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

作者： Liu, Ying Wu, Linzhi Univ Chinese Acad Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Geological disaster recognition, especially, landslide recognition, is of vital importance in disaster prevention, disaster monitoring and other applications. As more and more optical remote sensing images are available in recent years, landslide recognition on optical remote sensing images is in demand. Therefore, in this paper, we propose a deep learning based landslide recognition method for optical remote sensing images. In order to capture more distinct features hidden in landslide images, a particular wavelet transformation is proposed to be used as the preprocessing method. Next, a corrupting & denoising method is proposed to enhance the robustness of the model in recognize landslide features. Then, a deep auto-encoder network with multiple hidden layers is proposed to learn the high-level features and representations of each image. A softmax classifier is used for class prediction. Experiments are conducted on the remote sensing images from Google Earth. The experimental results indicate that the proposed wavDAE method outperforms the state-of-the-art classifiers both in efficiency and accuracy. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： target recognition deep learning remote sensing image

来源：评论

学校读者我要写书评

暂无评论

A Novel Fingerprint Classification Method Based on Deep Learning 23

A Novel Fingerprint Classification Method Based on Deep Lear...

引用

23rd International Conference on Pattern Recognition (ICPR)

作者： Wang, Ruxin Han, Congying Guo, Tiande UCAS Sch Math Sci Beijing 100049 Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781509048472

Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS). In recent years, deep learning is an emerging technology which has achieved great success in many fields, such as image processing, computer vision. In this paper, we have a preliminary attempt on the traditional fingerprint classification problem based on the new depth neural network method. For the fourclass problem, only choosing orientation field as the classification feature, we achieve 91.4% accuracy using the stacked sparse autoencoders (SAE) with three hidden layers in the NIST- DB4 database. And then two classification probabilities are used for fuzzy classification which can effectively enhance the accuracy of classification. By only adjusting the probability threshold, we get the accuracy of classification is 96.1% (setting threshold is 0.85), 97.2% (setting threshold is 0.90) and 98.0% (setting threshold is 0.95) with a single layer SAE. Applying the fuzzy method, we obtain higher accuracy.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

A model-based demand-balancing control for dynamically divided multiple urban subnetworks

引用

JOURNAL OF ADVANCED TRANSPORTATION 2016年第6期50卷 1046-1060页

作者： Lin, Shu Kong, Qing-Jie Huang, Qingming Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

Traffic control is an effective and efficient method for the problem of traffic congestion. It is necessary to design a high-level controller to regulate the network traffic demands, because traffic congestion is not only caused by the improper management of the traffic network but also to a great extent caused by excessive network traffic demands. Therefore, we design a demand-balance model predictive controller based on the macroscopic fundamental diagram-based multi-subnetwork model, which can optimize the network traffic mobility and the network traffic throughput by regulating the input traffic flows of the subnetworks. Because the transferring traffic flows among subnetworks are indirectly controlled and coordinated by the demand-balance model predictive controller, the subnetwork division can variate dynamically according to real traffic states, and a global optimality can be achieved for the entire traffic network. The simulation results show the effectiveness of the proposed controller in improving the network traffic throughput. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： traffic network control urban road network macroscopic fundamental diagram traffic flow equilibrium

来源：评论

学校读者我要写书评

暂无评论

How does green credit affect carbon emissions in China? A theoretical analysis framework and empirical study

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第39期29卷 59712-59726页

作者： Hu, Yi Zheng, Jiayu Univ Chinese Acad Sci Sch Econ & Management Zhongguancun South Yitiao Rd Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management 80 Zhongguancun East Rd Beijing 100190 Peoples R China

As an important part of China's green finance, green credit is regarded as an important tool to promote China's transformation to a low-carbon economy. In order to clarify the mechanism of green credit on carbon emissions, this paper puts forward a theoretical analysis framework including "functional attributes - micro subject response - key influencing factors" from the macro and micro perspectives. We select the panel data of 30 provinces in China from 2005 to 2019 for an empirical test and identify the action paths of green credit on carbon emission based on the mediating effect model. Further, we consider the special mechanism of "signal formation" and test it based on the dynamic panel threshold model. The results show that: (1) China's green credit mainly inhibits carbon emissions through three paths: industrial structure, energy structure and energy intensity. (2) There is a signal formation mechanism for the impact of green credit on carbon emissions, which mainly acts on the two action paths of industrial structure and energy intensity. (3) The signal formation mechanism is heterogeneous in each province. According to the empirical results, we divide the provinces into three echelons and propose corresponding suggestions.

关键词： Green credit Carbon emissions Theoretical analysis framework Mediating effect model Dynamic panel threshold model

来源：评论

学校读者我要写书评

暂无评论

SpikeODE: Image Reconstruction for Spike Camera With Neural Ordinary Differential Equation

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第11期34卷 11142-11155页

作者： Yang, Chen Li, Guorong Wang, Shuhui Su, Li Qing, Laiyun Huang, Qingming Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Sch Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100190 Peoples R China

The recently invented retina-inspired spike camera has shown great potential for capturing dynamic scenes. However, reconstructing high-quality images from the binary spike data remains a challenge due to the existence of noises in the camera. This paper proposes SpikeODE, a novel approach to reconstructing clear images by exploring temporal-spatial correlation to depress noises. The main idea of our method is to restore the continuous dynamic process of real scenes in a latent space and learn the temporal correlations in a fine-grained manner. Furthermore, to model the dynamic process more effectively, we design a conditional ODE where the latent state of each timestamp is conditioned on the observed spike data. Subsequently, forward and backward inferences are conducted through the ODE to investigate the correlations between the representation of the target timestamp and the information from both past and future contexts. Additionally, we incorporate a Unet structure with a pixel-wise attention mechanism at each level to learn spatial correlations. Experimental results demonstrate that our method outperforms state-of-the-art methods across several metrics.

关键词： Spike camera image reconstruction neural ordinary differential equation temporal-spatial correlation

来源：评论

学校读者我要写书评

暂无评论

Efficient Algorithms for Personalized PageRank Computation: A Survey

引用

IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2024年第9期36卷 4582-4602页

作者： Yang, Mingji Wang, Hanzhi Wei, Zhewei Wang, Sibo Wen, Ji-Rong Renmin Univ China Beijing 100872 Peoples R China Pazhou Lab Huangpu Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Methods MOE Key Lab Data Engn & Knowledge Engn Guangzhou 510555 Guangdong Peoples R China Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China

Personalized PageRank (PPR) is a traditional measure for node proximity on large graphs. For a pair of nodes s and t, the PPR value pi(s)(t)equals the probability that an alpha-discounted random walk from s terminates at t and reflects the importance between s and tin a bidirectional way. As a generalization of Google's celebrated PageRank centrality, PPR has been extensively studied and has found multifaceted applications in many fields, such as network analysis, graph mining, and graph machine learning. Despite numerous studies devoted to PPR over the decades, efficient computation of PPR remains a challenging problem, and there is a dearth of systematic summaries and comparisons of existing algorithms. In this paper, we recap several frequently used techniques for PPR computation and conduct a comprehensive survey of various recent PPR algorithms from an algorithmic perspective. We classify these approaches based on the types of queries they address and review their methodologies and contributions. We also discuss some representative algorithms for computing PPR on dynamic graphs and in parallel or distributed environments.

关键词： Vectors Surveys Clustering algorithms Heuristic algorithms Classification algorithms Reviews Measurement uncertainty Graphs and networks graph algorithms PageRank personalized PageRank

来源：评论

学校读者我要写书评

暂无评论

Uncertain random logic and uncertain random entailment

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2017年第5期8卷 695-706页

作者： Liu, Yuhan Yao, Kai Univ Cincinnati Dept Math Sci Cincinnati OH 45221 USA Univ Chinese Acad Sci Sch Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Probabilistic logic and uncertain logic, as two branches of the multi-valued logic, have been proposed for dealing with knowledge with random factors and with human uncertainty, respectively. As a generalization, this paper proposes an uncertain random logic to deal with complex knowledge containing random factors and human uncertainty simultaneously, and derives a formula to calculate the truth value of an uncertain random proposition. As an inverse problem of the uncertain random logic, this paper also proposes an uncertain random entailment model which calculates the truth value of a function of some uncertain random propositions based on the truth values of some other functions of these uncertain random propositions. As a byproduct, a probabilistic entailment model is also discussed.

关键词： Uncertain random variable Chance theory Propositional logic Entailment model

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：