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在20世纪五六十年代,"人工智能"这个术语就早已被正式提出。经历了几十个年代的发展,在Alpha Go击败李世乭时,人工智能(Artificial Intelligence)又受到了学者们的广泛关注和研究,同时机器学习(Machine Learning)和深度学习(deep learning)也相应的被提及到,甚至作为了人工智能其中的一个发展方向去拓展。本文对机器学习和深度学习的概念进行了解释与区分,从实际应用出发阐述了机器学习和深度学习的方向与应用,以及机器学习算法的分类。鉴于没有系统的学习过,可能在许多地方会有出入,还望更多的人能够有自己的思考。 相似文献
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以CNKI期刊数据库收录的(2001-2011年)有关图书馆服务创新领域的1 530条文献为研究对象,借助知识可视化软件——CiteSpace Ⅱ,绘制出热点词汇图谱、前沿词汇图谱以及文献共引网络图谱,利用可视化的方法对服务创新领域的研究热点、研究前沿及重要文献进行直观展示,从中发现近十年图书馆服务创新研究热点主要集中于信息服务、网络环境、知识服务等,前沿热点主要是知识经济、图书馆服务等。 相似文献
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人工智能和统计学的发展衍生出了关于数据分析的机器学习.这门新兴学科是数据分析领域的研究人员重点探究的方向.有科学家将机器学习定义为,计算机通过储存的数据经验不断提高其自身性能的行为.机器学习是指计算机通过固有的规律性信息获得新的经验和知识,从而提升计算机的智能,达到像人类一样作出决策的目的.随着计算机科学的发展,机器学习的探究和应用取得了很大成就,研究机器学习的数学理论和算法对计算机的发展有重要作用.由史斌和艾扬格编著、机械工业出版社出版的《机器学习的数学理论》一书,详细介绍了机器学习的数学理论,讨论了机器学习的优化理论,为提高计算机研究人员对于机器的认知、实现机器的自动化具有重要指导价值. 相似文献
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基于Citespace的商务智能研究热点与前沿可视化分析 总被引：2，自引：0，他引：2
分析Web of Science中以商务智能为主题的文献的时间和地域分布,借助Citespace软件绘制商务智能领域关键节点知识图谱,对关键节点文献进行共被引分析,通过Citespace关键词聚类和膨胀词探测技术,绘制出商务智能研究热点和前沿知识图谱,确定商务智能的热点研究领域和前沿发展趋势。 相似文献
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对CSSCI数据库中有关我国图书馆联盟研究的数据,利用CiteSpaceⅡ分析软件工具,以知识图谱的方式,梳理了图书馆联盟的知识基础、研究前沿以及未来发展趋势。研究发现,该领域主要研究学者为戴龙基、燕金伟、林嘉等;研究该领域的主要期刊有《中国图书馆学报》《情报杂志》和《图书情报工作》;研究前沿主要集中在图书馆联盟的管理研究、区域性研究和绩效评价研究三个方面;未来研究重点主要集中在区域性图书馆联盟研究。 相似文献
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基于Web of Science的国际ERP研究热点与研究前沿的信息可视化分析 总被引：2，自引：0，他引：2
以Web of Science(SCI-EXPANDED,SSCI,A&HCI,CPCI-S,CPCI-SSH)1986-2008年发表的全部466篇文献题录作为数据样本,通过高频主题词的分析确定国际ERP研究的热点领域,通过检测词频变动趋势显著的主题词确定国际ERP研究的前沿领域和发展趋势;并利用信息可视化软件Citespace,绘制出国际ERP研究热点与研究前沿的知识图谱。 相似文献
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以WebofScience数据库中《植物细胞》杂志自1989年创刊号到2012年底的所有文献作为研究对象,运用CiteS-paceII软件进行文献共引分析和共词分析。以知识可视化图谱方式展现20多年来植物科学领域的研究机构、知识基础、研究热点及研究前沿。 相似文献
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机器学习技术在自然语言处理中的应用是一个研究热点。简单介绍并分析、评价了机器学习的方法之一--基于实例学习。就其在自然语言处理关键环节之一--浅层句法分析方面进行实验研究并分析其结果。最后,讨论了基于实例学习在自然语言处理中的应用。 相似文献
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The application of the network technology in the power grid makes the Load Frequency Control (LFC) system more vulnerable to various kinds of network attacks. The Denial of Service (DOS) attack can block the data collected by the Phasor measurement unit from being transmitted to the LFC center, thereby affecting the decision of the control center and generation of control signals, and can not adjust the frequency of the power grid timely. Aiming at the DOS attack on LFC, a defense method based on data prediction is proposed. Through the combination of the deep learning algorithm and the Extreme Learning Machine (ELM) algorithm, the Deep auto-encoder Extreme Learning Machine (DAELM) algorithm combines the advantages of the fast speed of the extreme learning machine and the advantages of high accuracy of the deep learning. We can predict and supplement the lost data based on the DAELM algorithm, and ensure the normal operation of the LFC system, thus can prevent DOS attacks. The experiments verified the effectiveness of the proposed method. 相似文献
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Chuan Chen Rui Li Lin Shu Zhiyu He Jining Wang Chengming Zhang Huanfei Ma Kazuyuki Aihara Luonan Chen 《国家科学评论(英文版)》2020,7(6):1079
Predicting time series has significant practical applications over different disciplines. Here, we propose an Anticipated Learning Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data. From non-linear dynamical systems theory, we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable, thereby overcoming the small-sample problem and achieving multistep-ahead predictions. Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable, it is called anticipated learning. Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods. In contrast to traditional statistics-based machine learning, ALM is based on non-linear dynamics, thus opening a new way for dynamics-based machine learning. 相似文献
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《Information processing & management》2020,57(3):102210
This paper presents a binary classification of entrepreneurs in British historical data based on the recent availability of big data from the I-CeM dataset. The main task of the paper is to attribute an employment status to individuals that did not fully report entrepreneur status in earlier censuses (1851–1881). The paper assesses the accuracy of different classifiers and machine learning algorithms, including Deep Learning, for this classification problem. We first adopt a ground-truth dataset from the later censuses to train the computer with a Logistic Regression (which is standard in the literature for this kind of binary classification) to recognize entrepreneurs distinct from non-entrepreneurs (i.e. workers). Our initial accuracy for this base-line method is 0.74. We compare the Logistic Regression with ten optimized machine learning algorithms: Nearest Neighbors, Linear and Radial Support Vector Machine, Gaussian Process, Decision Tree, Random Forest, Neural Network, AdaBoost, Naive Bayes, and Quadratic Discriminant Analysis. The best results are boosting and ensemble methods. AdaBoost achieves an accuracy of 0.95. Deep-Learning, as a standalone category of algorithms, further improves accuracy to 0.96 without using the rich text-data that characterizes the OccString feature, a string of up to 500 characters with the full occupational statement of each individual collected in the earlier censuses. Finally, and now using this OccString feature, we implement both shallow (bag-of-words algorithm) learning and Deep Learning (Recurrent Neural Network with a Long Short-Term Memory layer) algorithms. These methods all achieve accuracies above 0.99 with Deep Learning Recurrent Neural Network as the best model with an accuracy of 0.9978. The results show that standard algorithms for classification can be outperformed by machine learning algorithms. This confirms the value of extending the techniques traditionally used in the literature for this type of classification problem. 相似文献
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[目的/意义]机器学习作为人工智能的关键核心技术,受到了前所未有的重视和快速发展。深入研究其发展现状和竞争格局,有助于为企业战略和相关产业政策制定提供科学决策依据。[方法/过程]基于DⅡ数据库和WOS数据库,从发展阶段、热点与核心领域识别、竞争国家对比三方面,对该技术领域发展现状、竞争格局进行了分析。[结果/结论]机器学习技术处于快速成长期,我国目前也处于快速发展期;我国在技术结构布局上存在短板;美国的专利活动最强,我国也属于技术活跃者;美国的专利质量最高,我国与其相差较大;互联网企业是重要推动力量;热点领域有智能诊断、自动驾驶仪、教育辅助、语音识别、计算机视觉等;核心领域有排序、学习、知识处理、搜索、模糊逻辑系统、专家系统等。 相似文献
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本文主要介绍了智能教学系统中的机器自学习机制,研究如何提高智能教学系统的智能性和通用性等方面的问题。文章采用基于信息论的示例学习,改进了决策树学习算法,并建立了机器学习决策树。 相似文献
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《Information processing & management》2022,59(2):102845
Cognitive impairments like memory disorder and depressive disorders lead to fatal consequences if proper attention is not given to such health hazards. Their impact is extended to the socioeconomic status of the developed and low or middle-income countries in terms of loss of talented and skilled population. Additionally, financial burden is borne by the countries in terms of additional health budget allotment. This paper presents a novel strategy for early detection of cognitive deficiency to eliminate the economic repercussions caused by memory disorder and depressive disorders. In this work, Electroencephalogram (EEG) and a word learning neuropsychological test, i.e. California Verbal Learning Task (CVLT), are conjunctively used for memory assessment. The features of EEG and scores of CVLT are modeled by applying different machine learning techniques, namely K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Comparatively, experimental results have better classification accuracy than the existing schemes that considered EEG for estimating cognitive heuristics. More specifically, SVM attains the highest accuracy score of 81.56% among all machine learning algorithms, which can assist in the early detection of cognitive impairments. The proposed strategy can be helpful in clinical diagnosis of psychological health and improving quality of life as a whole. 相似文献
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支持向量机(SVM)是建立在统计学习理论基础上的一种通用的研究机器学习规律的方法。它具有很强的学习能力和泛化能力,可以有效地处理分类,回归等问题。SVM在处理非线性问题时,通过使用一个核函数来解决复杂计算问题。最小二乘支持向量机(LS_SVM)是SVM的一种改进,它提高了求解问题的速度和收敛精度。本文以太阳黑子为数据集,基于LS_SVM工具,使用了支持向量回归算法(SVR),实现了太阳黑子活动的预测。 相似文献
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Machine learning tools are increasingly infiltrating everyday work life with implications for workers. By looking at machine learning tools as part of a sociotechnical system, we explore how machine learning tools enforce oppression of workers. We theorize, normatively, that with reorganizing processes in place, oppressive characteristics could be converted to emancipatory characteristics. Drawing on Paulo Freire’s critical theory of emancipatory pedagogy, we outline similarities between the characteristics Freire saw in oppressive societies and the characteristics of currently designed partnerships between humans and machine learning tools. Freire’s theory offers a way forward in reorganizing humans and machine learning tools in the workplace. Rather than advocating human control or the decoupling of workers and machines, we follow Freire’s theory in proposing four processes for emancipatory organizing of human and machine learning partnership: 1) awakening of a critical consciousness, 2) enabling role freedom, 3) instituting incentives and sanctions for accountability, and 4) identifying alternative emancipatory futures. Theoretical and practical implications of this emancipatory organizing theory are drawn. 相似文献