Deep neural networks and transfer learning applied to multimedia web mining

López-Sánchez, Daniel; Arrieta, Angélica González; Corchado, Juan M.

doi:10.1007/978-3-319-62410-5_15

Cited by 19 publications

(13 citation statements)

References 5 publications

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“…This is essential to mobile network environments, as they require to agilely respond to new network patterns and threats. A number of important applications emerge in the computer network domain [57], such as Web mining [550], caching [551] and base station sleep strategies [207]. There exist two extreme transfer learning paradigms, namely one-shot learning and zero-shot learning.…”

Section: Tailoring Deep Learning To Changing Mobile Network Enviromentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,328

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Section: Tailoring Deep Learning To Changing Mobile Network Enviromentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,328

838

View full text Add to dashboard Cite

show abstract

“…Lopez et al [257] Transfer Learning and t-SNE Used transfer learning for multimedia web mining and t-SNE for dimensionality reduction and visualization of web mining resultant model.…”

Section: Internet Trafficmentioning

confidence: 99%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

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While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

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“…In the machine learning problems, domain transfer learning has recently attracted much attention Lopez-Sanchez, Arrieta and Corchado, 2018;Wang, Zhou, Kong, Currey, Li and Zhou, 2017a;Wang, Song, Marquez-Lago, Leier, Li, Lithgow, Webb and Shen, 2017b;Yang and Zhang, 2017;Zhang, Yang and Zhang, 2017c). Transfer learning refers to the learning problem of a predictive model for a target domain, by leveraging the data from both the target domain and one or more auxiliary domains.…”

Section: Backgroundsmentioning

confidence: 99%

Domain transfer convolutional attribute embedding

Wang

2018

Journal of Experimental & Theoretical Artificial Intelligen

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In this paper, we study the problem of transfer learning with the attribute data. In the transfer learning problem, we want to leverage the data of the auxiliary and the target domains to build an effective model for the classification problem in the target domain. Meanwhile, the attributes are naturally stable cross different domains. This strongly motives us to learn effective domain transfer attribute representations. To this end, we proposed to embed the attributes of the data to a common space by using the powerful convolutional neural network (CNN) model. The convolutional representations of the data points are mapped to the corresponding attributes so that they can be effective embedding of the attributes. We also represent the data of different domains by a domain-independent CNN, ant a domain-specific CNN, and combine their outputs with the attribute embedding to build the classification model. An joint learning framework is constructed to minimize the classification errors, the attribute mapping error, the mismatching of the domain-independent representations cross different domains, and to encourage the the neighborhood smoothness of representations in the target domain. The minimization problem is solved by an iterative algorithm based on gradient descent. Experiments over benchmark data sets of person re-identification, bankruptcy prediction, and spam email detection, show the effectiveness of the proposed method.

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Deep neural networks and transfer learning applied to multimedia web mining

Cited by 19 publications

References 5 publications

Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Domain transfer convolutional attribute embedding

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