Semantic Scholar Open Access 2016 833 sitasi

A survey of machine learning for big data processing

Junfei Qiu Qi-hui Wu Guoru Ding Yuhua Xu S. Feng

Lihat Sumber DOI

Abstrak

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

Topik & Kata Kunci

Computer Science

Penulis (5)

Junfei Qiu

Qi-hui Wu

Guoru Ding

Yuhua Xu

S. Feng

Format Sitasi

APA MLA BibTeX

Qiu, J., Wu, Q., Ding, G., Xu, Y., Feng, S. (2016). A survey of machine learning for big data processing. https://doi.org/10.1186/s13634-016-0355-x

Akses Cepat

Lihat di Sumber doi.org/10.1186/s13634-016-0355-x

Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Total Sitasi: 833×
Sumber Database: Semantic Scholar
DOI: 10.1186/s13634-016-0355-x
Akses: Open Access ✓