Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning. Группа авторов
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СКАЧАТЬ 9–26.

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       Nur Zincir‐Heywood1, Marco Mellia2, and Yixin Diao3

       1 Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

       2 Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy

       3 PebblePost, New York, NY, USA

      On the other hand, ML is data driven [8]. It means programming to optimize performance criteria using examples of data or past experience. In ML, there exists a model defined by some parameters, then the learning becomes the execution of a program to optimize the parameters of the model using the training data or the past experience. Past experience case is distinct from either supervised or unsupervised learning because credit assignment is subject to delays. Thus, it is not immediately apparent which behaviors should be rewarded or penalized. This issue is specific to reinforcement learning. The model could be predictive to make predictions in the future, or it could be descriptive to gain knowledge from data, or it could be both. ML uses statistical theory to guide model building in order to infer from the training data or the past experience. In training, efficient algorithms are necessary to solve the optimization problem as well as to store and process the data/past experience. Moreover, the model that is learned at the end of training is required to have efficient representation and solution for inference purposes, possibly in real time. In some applications of ML, the efficiency of the learning and inference model, in other words the space and time complexity could be as important as its prediction accuracy. The growth of network technologies for easy access to data, cheaper access to CPU power, and fast access to data storage has enabled the use of ML algorithms in network and service management [9, 10].

      2.2.1 Supervised Learning