检索结果-内蒙古大学图书馆

An efficient reachability query based pruning algorithm in e-health scenario

JOURNAL OF BIOMEDICAL INFORMATICS 2019年 94卷 103171-000页

作者： Mondal, Fikureshi Mukherjee, Nandini Narula Inst Technol Dept Comp Sci & Engn Kolkata 700109 W Bengal India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India

We propose a Disease - Symptom graph database for our mobile-assisted e-healthcare application. A large Disease - Symptom graph is stored in the cloud and accessed using mobile devices over the Internet. Query and search are the fundamental operations of graph databases. However, while searching the Disease - Symptom graph for making preliminary diagnosis of diseases, queries become complex due to the complex structure of data and also queries are too hard to write and interpret. Moreover, it is not possible to access the graph frequently due to limited bandwidth of the network, transmission delay, and higher cost. Subgraph generation or pruning algorithm for appropriate inputs is one of the solutions to this problem. In this paper, we propose an efficient pruning algorithm by introducing a new approach to decompose the Disease - Symptom graph into a series of symptom trees (ST). All the Symptom trees are merged to build a pruned subgraph which is our requirement. We demonstrate the efficiency and effectiveness of our pruning algorithm both analytically and empirically and validate on Disease - Symptom graph database, as well as other real graph databases. Also a comparison is done with an efficient existing reachability based Chain Cover algorithm after modifying it ChainCoverPrune as pruning algorithm. These two algorithms are tested for storage and access parametric measures for querying the synthetic and real directed databases to show the efficiency of the proposed algorithm.

关键词： Disease - Symptom graph database Reachability Query Chain Cover pruning algorithm

来源：评论

学校读者我要写书评

暂无评论

A Voronoi-Diagram-based method for centerline extraction in 3D industrial line-laser reconstruction using a graph-centrality-based pruning algorithm

引用

OPTIK 2022年 261卷

作者： Chen, Cheng Mei, Xuesong Hou, Dongxiang Fan, Zhengjie Huang, Wangwang Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China

Three-dimensional (3D) line-laser scanning is a widely used 3D reconstruction technique in the industry. As a key procedure of 3D line-laser scanning, centerline extraction of laser stripes directly determines the accuracy of reconstructed 3D models. Because of the noise inside laser stripes, centerline extraction methods based on the gray distribution may provide biased results. In order to address this problem, a Voronoi-diagram-based method (VM) for centerline extraction, which can extract centerlines accurately under severe noises, is proposed. To solve the emerging problems when the Voronoi diagram is applied to line-laser stripes, a fast pruning algorithm based on the distribution of graph centrality is proposed, and two centerline extension algorithms based on least square fitting are developed. The experiments are performed on synthetic images and a line-laser 3D scanner to evaluate the method's accuracy, robustness, and efficiency. The VM method is proved to have better accuracy and robustness than the traditional method. Simulation experiments show that the VM can extract centerlines from noisy images with an average accuracy of 0.35 pixels. Also, 3D reconstruction experiments of a phi 20-mm standard sphere demonstrate an average accuracy of 0.0282 mm. With four-thread acceleration, the proposed method can process images with a resolution of 2448 x 2048 pixels in 0.5 s. The accuracy and speed of the proposed method can be adjusted by changing the parameter related to the density of contour points, which makes this method flexible and widely applicable in applications with different requirements.

关键词： Voronoi diagram Centerline extraction pruning algorithm Graph centrality 3D line-laser reconstruction

来源：评论

学校读者我要写书评

暂无评论

Design and improvement of the pruning algorithm of the Chinese chess in the computer games

引用

JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 426-428页

作者： Tao, Jun Wu, Gui Pan, Xueliang Jianghan Univ Sch Math & Comp Sci Wuhan 430056 Peoples R China Rowan Univ Dept Elect & Comp Engn Glassboro NJ 08028 USA Jianghan Univ Educ Adm Off Wuhan 430056 Peoples R China Jianghan Univ Grad Sch Wuhan 430056 Peoples R China

Computer games have always been considered to be the most challenging task in the field of artificial intelligence specially the Chinese chess as an example. The core technology of the computer games is the search. This work is studied to research an improved pruning strategy in order to achieve a deeper level search of the game tree in a limited time. On the basis of the traditional alpha-beta search algorithm, the worthless nodes are discarded to be never searched by introducing the deep iterative, history table and other auxiliary methods of pruning. The number of nodes searched is effectively reduced to make the pruning earlier to shorten the search time. At the same time, its search depth is higher than the original search algorithm. The advanced and modified algorithm is proved to be practical and applicative by experimentations and tests of computer games system provided in this study.

关键词： tree searching computer games artificial intelligence computer games system pruning algorithm Chinese chess pruning strategy level search game tree alpha-beta search algorithm artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Structure optimization strategy of Neural Networks - Research on pruning algorithm

Structure optimization strategy of Neural Networks - Researc...

引用

3rd World Congress on Intelligent Control and Automation

作者： Zhang, M Xu, YM Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)7312012035

We analysis the major factors which influence the generalization performance of the Neural Networks, make a survey on structure optimization methods and present several kinds of pruning algorithms to simulate with the MLFN on two kinds of typical problems. The result suggests that pruning algorithm can effectively optimize the structure of Neural Networks and notably improve their generalization. At the end of the paper, some new development of the pruning algorithm is summarized.

关键词： pruning algorithm Neural Networks MLFN generalization structure optimization

来源：评论

学校读者我要写书评

暂无评论

Application of pruning algorithm on Voice Service Platform of Cow Disease Diagnosis Expert System

引用

Journal of Northeast Agricultural University(English Edition) 2009年第4期16卷 47-51页

作者： WANG Lishu Engineering College Northeast Agricultural University- Harbin 150030 China

Expert System （ES） is considered effective and efficient in agricultural production, as agricultural informationization becomes a main trend in agricultural development. ES, however, is applied unsatisfactorily in most rural areas of China and it has considerably affected and restricted the development of the agricultural informationization. This paper proposed a solution to voice service system of ES, which was suitable for the information transmission, and it especially could help the peasants in remote regions obtain knowledge from ES through the voice service system. As for the disadvantages of massive knowledge data and slow deduction, in this system the classification method could be adopted based on the decision tree. Designing pruning algorithm to ＂trim off＂ the unrelated knowledge to the users in query course would simplify the structure of the decision tree and accelerate the speed of deduction before the inference engine deduced the knowledge required by users.

关键词： pruning algorithm voice service platform cow disease Expert System

来源：评论

学校读者我要写书评

暂无评论

Connection Weights Sparselization and pruning algorithm in Higher Order Neural Networks

Connection Weights Sparselization and Pruning Algorithm in H...

引用

1998 International Conference on Neural Networks and Brain(ICNN&B’98)

作者： Li Wangchao Li Wenjing Zhang Jie Jinyan Li Hebei University of Technology The University of Melbourne

In this paper,the fully-connected higherorder neuron and sparselized higher-order neuron are introduced,the mapping capabilities of the fullyconnected higher-order neural networks are investigated, and that arbitrary Boolean function defined from {0,1} can be realized by fully-connected higher-order neural networks is *** on this,in order to simplify the networks' architecture,a pruning algorithm of eliminating the redundant connection weights is also proposed,which can be applied to the implementation of sparselized higher-order neural classifier and other *** simulated results show the effectiveness of the algorithm.

关键词： Higher-order neural networks Redundant connection weights Sparselized pruning algorithm Neural classifier

来源：评论

学校读者我要写书评

暂无评论

Construction of Decision Analysis System Based on Improved Decision Tree pruning algorithm and Rough Set Classification Theory

Construction of Decision Analysis System Based on Improved D...

引用

2019 9th International Conference on Education and Social Science(ICESS 2019)

作者： Lan Wang Hongsheng Xu Luoyang Normal University

Decision trees are generally generated from top to bottom,and each decision or event may lead to two or more *** set is characterized by the use of imprecise,uncertain,partial real information to obtain easy to process,robust,low-cost decision-making *** tree method is widely used in enterprise *** sets use upper and lower approximations to describe uncertainty,which makes the boundary clear and reduces the randomness of algorithm *** paper presents construction of decision analysis system based on improved decision tree pruning algorithm and rough set classification Theory.

关键词： pruning algorithm Decision tree Rough set C4.5 algorithm Classification reduction

来源：评论

学校读者我要写书评

暂无评论

pruning boosted classifiers with a real valued genetic algorithm

引用

KNOWLEDGE-BASED SYSTEMS 1999年第5-6期12卷 277-284页

作者： Thompson, S BT Labs Intelligent Business Syst Res Grp Ipswich IP5 7RE Suffolk England

Ensemble classifiers and algorithms for learning ensembles have recently received a great deal of attention in the machine learning literature (R.E. Schapire, Machine Learning 5(2) (1990) 197-227;N. Cesa-Bianchi, Y. Freund, D. Haussler, D.P. Helbold, R.E. Schapire, M.K. Warmuth, Proceedings of the 25th Annual ACM Symposium on the Theory of Computing, 1993, pp. 382-391;L. Breiman, Bias, Technical Report 460, Statistics Department, University of California, Berkeley, CA, 1996;J.R. Quinlan, Proceedings of the 14th International Conference on Machine Learning, Italy, 1997;Y. Freund, R.E. Schapire, Proceedings of the 13th International Conference on Machine Learning ICML96, Bari, Italy 1996, pp. 148-157;A.J.C. Sharkey, N.E. Sharkey, Combining diverse neural nets, The Knowledge Engineering Review 12 (3) (1997) 231-247). In particular, boosting has received a great deal of attention as a mechanism by which an ensemble of classifiers that has a better generalisation characteristic than any single classifier derived using a particular technique can be discovered. In this article, we examine and compare a number of techniques for pruning a classifier ensemble which is overfit on its training set and find that a real valued GA is at least as good as the best heuristic search algorithm for choosing an ensemble weighting. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： ensemble classifier pruning boosted classifiers pruning algorithm backfitting procedure

来源：评论

学校读者我要写书评

暂无评论

A Matrix Iteration algorithm With pruning for Pinpointing Multivariate Correlations From High Dimensional Data Sets

引用

IEEE ACCESS 2021年 9卷 5150-5165页

作者： Shao, Fubo Hou, Zhiqiang Jia, Limin Zhang, Zhe Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Post Doctor Stn CRRC Corp Ltd Beijing 100067 Peoples R China CRRC Acad Beijing 100070 Peoples R China China Waterborne Transport Res Inst Beijing 100088 Peoples R China

There are a few dependent multivariate relationships among high dimensional data sets. Then how to identify these dependent variables from high dimensional data sets is an important issue for data analysis. Now, the most frequently used method is the enumeration method, that is all multivariate relationships in the high dimensional data sets are examined. However, the time complexity of the enumeration method is exponential (2(n)) and the calculation load is very heavy when the dimension is high. Aiming at solving this problem, the matrix iteration algorithm with pruning (MIP) is proposed for pinpointing multivariate dependent relationships in high dimensional data sets without examining all multivariate relationships. Some not dependent relationships are ignored without examined by the pruning process of the proposed MIP and the computing burden is reduced. The maximal information coefficient (MIC) is adopted as the measure of correlations in the proposed MIP algorithm due to the excellent properties, generality and equitability, of MIC. In the case of the data set with 5 variables, more than 50% multivariate relationships are pruned without examining. Numerical experiments also show that the calculating burden is greatly reduced. Compared to the enumeration method, 82:5% calculating time and 98:5% calculating times of multivariate relationships are saved for the data set with two dependent multivariate relationships among 30 variables in the experiment. The proposed MIP algorithm is effective for pinpointing multivariate dependent relationships from data sets with high dimensions.

关键词： Correlation Microwave integrated circuits Mutual information Approximation algorithms Random variables Kernel Estimation Correlation high dimensions maximal information coefficient pruning algorithm

来源：评论

学校读者我要写书评

暂无评论

A new growing pruning deep learning neural network algorithm (GP-DLNN)

引用

NEURAL COMPUTING & APPLICATIONS 2020年第24期32卷 18143-18159页

作者： Zemouri, Ryad Omri, Nabil Fnaiech, Farhat Zerhouni, Noureddine Fnaiech, Nader HESAM Univ CNAM Cedric Lab 292 Rue St Martin F-750141 Paris 03 France Univ Bourgogne Franche Comte FEMTO ST Besancon France Univ Tunis LR13ES03 SIME ENSIT Tunis Tunisia ISIMa Inst Super Informat Mahdia Hiboun Tunisia

During the last decade, a significant research progress has been drawn in both the theoretical aspects and the applications of Deep Learning Neural Networks. Besides their spectacular applications, optimal architectures of these neural networks may speed up the learning process and exhibit better generalization results. So far, many growing and pruning algorithms have been proposed by many researchers to deal with the optimization of standard Feedforward Neural Network architectures. However, applying both the growing and the pruning on the same net may lead a good model for a big data set and hence good selection results. This work is devoted to propose a new Growing and pruning Learning algorithm for Deep Neural Networks. This new algorithm is presented and applied on diverse medical data sets. It is shown that this algorithm outperforms various other artificial intelligent techniques in terms of accuracy and simplicity of the resulting architecture.

关键词： Growing algorithm pruning algorithm Constructive neural networks Deep learning Deep neural networks

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：