To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modi...To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.展开更多
Machine learning(ML)has transformed numerous fields,but understanding its foundational research is crucial for its continued progress.This paper presents an overview of the major classical ML algorithms and examines t...Machine learning(ML)has transformed numerous fields,but understanding its foundational research is crucial for its continued progress.This paper presents an overview of the major classical ML algorithms and examines the state-of-the-art publications,spanning seventy decades,through an extensive bibliometric analysis.We analyzed a dataset of highly cited papers from prominent ML conferences and journals,employing techniques such as citation and keyword analyses to uncover key insights.The study further identifies the most influential papers and authors,reveals the evolving collaborative networks within the ML community,and pinpoints prevailing research themes and emerging areas of focus.Additionally,we examine the geographic distribution of highly cited publications,highlighting the leading countries in ML research.This study provides a comprehensive overview of the evolution of traditional learning algorithms,and their impacts and discusses challenges and opportunities for future development,with a particular focus on the Global South.The findings from this paper offer valuable insights for both ML experts and the broader research community,enhancing understanding of the field's trajectory and its significant influence on recent advances in learning algorithms.展开更多
基金supported in part by the National Natural Science Foundation of China (NO. 61401004, 61271233, 60972038)Plan of introduction and cultivation of university leading talents in Anhui (No.gxfxZ D2016013)+3 种基金the Natural Science Foundation of the Higher Education Institutions of Anhui Province, China (No. KJ2010B357)Startup Project of Anhui Normal University Doctor Scientific Research (No.2016XJJ129)the US National Science Foundation under grants CNS1702957 and ACI-1642133the Wireless Engineering Research and Education Center at Auburn University
文摘To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service(QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition(K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bagQoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoSwords, Locality Constrained Feature Coding(LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines(SVM) clas-sifier. Our experimental results demonstrate the feasibility of the proposed classification method.
文摘Machine learning(ML)has transformed numerous fields,but understanding its foundational research is crucial for its continued progress.This paper presents an overview of the major classical ML algorithms and examines the state-of-the-art publications,spanning seventy decades,through an extensive bibliometric analysis.We analyzed a dataset of highly cited papers from prominent ML conferences and journals,employing techniques such as citation and keyword analyses to uncover key insights.The study further identifies the most influential papers and authors,reveals the evolving collaborative networks within the ML community,and pinpoints prevailing research themes and emerging areas of focus.Additionally,we examine the geographic distribution of highly cited publications,highlighting the leading countries in ML research.This study provides a comprehensive overview of the evolution of traditional learning algorithms,and their impacts and discusses challenges and opportunities for future development,with a particular focus on the Global South.The findings from this paper offer valuable insights for both ML experts and the broader research community,enhancing understanding of the field's trajectory and its significant influence on recent advances in learning algorithms.
