Collaborative Intelligent Computing System:Theoretical Model with Its Application

本文探讨了协同智能计算系统的理论模型及其应用,通过间接计算模型与间接形式化方法结合产生了第三类信息处理方式。该系统不仅能处理计算机数据还能处理自然语言信息,揭示了中文自然语言理解的双重技术路线。

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SOFTWARE
(Published monthly since 1979)
Vol.32 No.6 June 2011
ISSN 1003-6970 CN12-1151/TP
Experts Forum
Collaborative Intelligent Computing System:Theoretical Model with Its Application……………………… ZOU Xiaohui, ZOU Shunpeng(1)


Collaborative Intelligent Computing System :Theoretical Model with Its Application

ZOU Xiaohui ZOU Shunpeng
Institute of Higher Education at China University of Geosciences (Beijing), Beijing 100083,China

【Abstract】 This study is aimed at revealing the view of Collaborative Intelligence derived from the mode of two types of Information Processing, namely computer data and human knowledge, as well as the theoretical model of Collaborative Intelligence Computing System with its application guided by this view. It involves a verifiable method. On the one hand, within the range of n 2 matrix, the two basic algorithms, which are enumeration based on divergence equivalent to the way of 2 n and search based on convergence equivalent to the way of 1/2 n , can be used as tasks of pure digital computing. It is characterized by satisfying the condition that the value-taking of n does not influence computational efficiency of the Indirect Computational Tasks; On the other hand, within the range of n 2 matrix corresponding to each grid, Indirect Formalization Processing is used towards single character, which is characterized not only by that single-Zi-syllable, namely Yan, can be calculated indirectly, but also by that two-Zi-syllable and multi-Zi-syllable, namely Yu, can also be calculated indirectly. At the same time, the frequencies of the reuse of Yan and Yu can both be easily counted and calculated indirectly. Thus, this model can not only lead us to revealing the double technique route toward Chinese Information Processing or Natural Language Understanding, but also to verifying the hypothesis of Informatics Basic Laws, which dominate the Indirect Computing Model and Indirect Formalization Method. Eventually, it can be concluded that the previous two verifiable empirical methods are much more than a simple sum of these modes of the two types of Information Processing, namely computer data and human knowledge, but the theoretical model of Collaborate Intelligent Computing System or the third kind of Information Processing Mode with its application generated by Rational Division of labor, highly Collaborative Synergy, such as academic forefront of various journals and conference abstracts home and abroad, answers to Frequent Answered Questions of a variety of software and their Help Files, individual records of Collaborative Intelligent Computing System users, limited symbols of natural language and its limited rules inherent in the diverse combination process or the reuse process and so on, which are the Computer-Aided Analysis of all kinds of Bilingual Information. The main target of such cloud-based computing is the customers who need Computer-Aided Bilingual Knowledge and Information Processing in various types of creative cooperative and productive activities, such as teaching, research, production, learning and using.
【Key words】Computer ; Indirect Computing; Indirect Formalization; Software

软 件
(Ruan Jian)
月刊(1979 年创刊)
2011 年6 月 第32 卷第6 期
ISSN1003-6970 CN12-1151/TP
《软件》杂志首届中文核心期刊,被《中国核心期刊(遴选)数据库》、《中国学术期刊综合评价数据库来源期刊》、《万方数据—数字化期刊群全文收录期刊》、《中文科技期刊数据库(全文版)收录期刊》、《中国知识资源总库CNKI 源期刊》、美国《乌利希国际期刊指南》、波兰《哥白尼索引》、美国《剑桥科学文摘》等国内外数据库收录

目 录

专家论坛

协同智能计算系统——理论模型及其应用*…………………………………………… 邹晓辉 邹顺鹏 (1)


协同智能计算系统——理论模型及其应用
邹晓辉 邹顺鹏
中国地质大学(北京)高等教育研究所 北京 10008
关键词 本研究工作目的是揭示计算机数据与自然人知识两类信息处理方式基础之上派生的协同智能观及其指导下的协同智能计算系统的理论模型及其应用。它涉及的可验证方法,一方面,在n 2矩阵范围内,以等价于2 n的发散方式枚举和以等价于1/2 n的收敛方式搜索两类基本算法可用作处理纯数字计算的任务,其特征是满足n的取值不影响计算效率的间接计算任务;另一方面,在与n 2矩阵各个格子一一对应的范围对单音节汉字进行间接形式化处理,其特征不仅在于单音节字,即言,可间接计算,而且,还在于双音节和多音节的字组,即语,也可间接计算,同时,言和语的复用频率均可以且便于间接计算和统计。其结果是:不仅中文的自然语言理解的双重技术路线被揭示,而且,支配这类间接计算模型与间接形式化方法的信息基本定律假说也可被验证。最终可得出这样的结论,即:在前述两方面可验证的两种实证方法,远不仅仅是计算机数据信息处理方式与自然人知识信息处理方式这两类信息处理方式的简单相加,而是这两者合理分工、高度协作所产生的协同智能计算系统的理论模型或第三类信息处理方式及其应用,例如:国内外学术前沿的各类期刊及会议论文摘要、各种软件的常用问题解答以及帮助文件、协同智能计算系统用户个性化记录、自然语言的有限符号及其多样化组合或重复使用过程中蕴含的有限规则、等等各类双语信息的计算机辅助分析,该类云端计算主要服务对象是在创造性合作型生产式教研产学用各类活动中需要计算机辅助双语知识信息数据处理服务的客户。

关键词 计算机;间接计算;间接形式化;软件
中图分类号TP18 文献标识码 A



中国知网:邹晓辉-协同智能计算系统——理论模型及其应用

(2011-10-04 16:28:28)

www.cnki.net/kcms/detail/12.1151.TP.20111004.1537.001.html

http://blog.sciencenet.cn/home.php?mod=space&uid=94143&do=blog&quickforward=1&id=493188

已有 10 次阅读 2011-10-4 14:36|个人分类:双语信息处理|系统分类:论文交流|关键词:模型 协同 推荐到群组

《软件》杂志 2011年 第6期

《软件》杂志 2011年 第6期

本文 旨在阐述协同智能计算系统的理论模型及其应用,其特征是间接计算模型与间接形式化方法 [1]的结合而产生第三类信息处理方式及其应用。它不仅仅是计算机数据信息处理方式与自然人知识信息处理方式这两类信息处理方式的简单相加,而是这两者相互之间的合理分工、高度协作所产生的协同智能 [2]计算系统的理论模型及其应用。
计算机科学技术界可以理解人们做这样的假设,即:如果人工智能 [3]是信息技术的皇冠,那么自然语言理解 [4]就是该皇冠上的一颗明珠。进一步也可以理解我们做这样的假设,即:如果自然人的大脑智能是第一智能,而计算机的电脑智能是第二智能,那么,基于整体大于局部之和的系统科学原理,是否可以把前两种智能的结合称之为第三智能 [5]呢?我们认为不仅可以这么说,而且还可以这么做的。本文一个研究切入点同时也是一个原创点就是从这个思路来做自然语言理解的,因此,才可能发现并确信自然语言理解存在着双重技术路线,进而,也才可能揭示第二路径的科学机理并发现其在双语信息处理上的妙用,这不仅涉及语言学基础研究 [6]一个突破,而且,也涉及信息学基础研究 [7]另一个突破,同时,还涉及教育学和管理学两个领域基础研究 [8] [9][10]的又一个突破。
这怎么可能呢?真是让人难以置信!但是,经过近十几年在上述几个相关研究领域的探索、研究和广泛的国际国内交流,最终让我们得到了确切的研究结果、结论和具体的应用示例。
下面就把难以置信变为确信无疑的探索历程介绍给读者



中国知网


阅读全文,由此进入:

《软件》杂志 2011年 第6期
《软件》杂志 2011年 第5期
This volume of proceedings provides an opportunity for readers to engage with a selection of refereed papers that were presented at the Third International Symposium on Intelligent Systems Technologies and Applications (ISTA’17). ISTA aims to bring together researchers in related fields to explore and discuss various aspects of intelligent systems technologies and their applications. This edition was hosted by Manipal Institute of Technology, Manipal University, Manipal, India, during September 13–16, 2017. ISTA’17 was colocated with the Second International Conference on Applied Soft Computing and Communication Networks (ACN’17). All submissions were evaluated on the basis of their significance, novelty, and technical quality. A double-blind review process was conducted to ensure that the author names and affiliations were unknown to the TPC. These proceedings contain 34 papers selected for presentation at the symposium. We are very grateful to the many people who helped with the organization of the symposium. Our sincere thanks go to all authors for their interest in the symposium and to the members of the Program Committee for their insightful and careful reviews, all of which were prepared on a tight schedule but still received in time. The conference could not have happened without the commitment of the Local Organizing Committee, who helped in many ways to assemble and run the con- ference. We are grateful to the General Chairs for their support. We express our most sincere thanks to all keynote speakers who shared with us their expertise and knowledge. Finally, we would like to acknowledge Springer for active cooperation and timely production of the proceedings.
协同捆绑推荐是一种通过深度学习算法将用户与物品集进行匹配的方法。这种方法可以为用户提供个性化的捆绑推荐,准确地满足用户的需求与兴趣。 在这种方法中,使用者的行为数据被采集并存储,例如购买记录、浏览历史、评分等。利用这些数据,可以建立一个用户-物品矩阵,其中行表示用户,列表示物品,矩阵元素则表示用户对物品的行为。通过分析用户-物品矩阵的模式与关联,可以预测用户对未来物品的偏好。 深度学习模型通常由多个层次的神经网络组成,通过学习和分类大量数据样本,能够提取出潜在的用户和物品特征。这些特征可以捕捉到更多的信息,用于计算用户与物品之间的相似度或相关度。通过比较用户特征与物品特征的差异,可以得出最合适的捆绑推荐。 协同捆绑推荐算法具有以下优点: 1. 个性化推荐:该算法可以根据用户的兴趣和偏好,为每个用户提供个性化的捆绑推荐,增加用户的满意度和体验。 2. 精准的匹配:通过深度学习算法,可以准确地计算用户与每个物品之间的匹配度,从而找到最佳的匹配项。 3. 时间效率:深度学习算法能够快速处理大规模的用户-物品矩阵,实现实时的捆绑推荐。 然而,协同捆绑推荐算法也存在一些挑战: 1. 数据稀疏性:用户对物品的行为数据通常是稀疏的,某些物品可能没有足够的数据支持,导致推荐的准确性有所降低。 2. 冷启动问题:当新用户或新物品加入系统时,缺乏充分的数据进行推荐,需要设计特殊的策略来解决这个问题。 3. 模型可解释性:深度学习算法在推荐过程中产生的结果往往难以解释,这可能对用户产生疑虑,影响用户的信任度。 综上所述,协同捆绑推荐算法通过深度学习模型实现用户与物品集之间的匹配,能够提供个性化且精准的捆绑推荐。然而,需要克服数据稀疏性、冷启动问题和模型可解释性等挑战,以进一步提高算法的效果和用户的满意度。
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