ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)||7|
|Section||Session 3: Computer|
|Published online||05 September 2017|
- H. Zhou, W. U. Wei, Q. Z. Teng, X. M. Yang, L. I. Min, and D. Y. Tao, “Research of manifold learning algorithm, “ Application Research of Computers, vol. 24, no. 7, pp. 214–217, 2007. [Google Scholar]
- R. Collobert and J. Weston, “A unified architecture for natural language processing:deep neural networks with multitask learning, “ in International Conference, 2008, pp. 160–167. [Google Scholar]
- B. Perozzi, R. Al-Rfou, and S. Skiena, “DeepWalk: online learning of social representations, “ pp. 701–710, 2014. [Google Scholar]
- R. Metzler and J. Klafter, “The random walk’s guide to anomalous diffusion: a fractional dynamics approach, “ Physics Reports, vol. 339, no. 1, pp. 1–77, 2000. [NASA ADS] [CrossRef] [Google Scholar]
- J. R. Shewchuk, “An introduction to the conjugate gradient method without the agonizing pain, “ Ph.D. dissertation, Carnegie Mellon University, 1994. [Google Scholar]
- J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei, “Line: Largescale information network embedding, “ in International Conference on World Wide Web, 2015, pp. 1067–1077. [CrossRef] [Google Scholar]
- S. Cao, W. Lu, and Q. Xu, “Grarep:learning graph representations with global structural information, “ 2015, pp. 891–900. [Google Scholar]
- A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks, “ 2016, p. 855. [Google Scholar]
- C. Yang, Z. Liu, D. Zhao, M. Sun, and E. Y. Chang, “Network representation learning with rich text information, “ in International Conference on Artificial Intelligence, 2015, pp. 2111–2117. [Google Scholar]
- J. Chen, Q. Zhang, and X. Huang, “Incorporate group information to enhance network embedding, “ in ACM International on Conference on Information and Knowledge Management, 2016, pp. 1901–1904. [Google Scholar]
- J. Li, A. Ritter, and J. Dan, “Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks,” Computer Science, 2015. [Google Scholar]
- D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation, “ Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003. [Google Scholar]
- Q. V. Le and T. Mikolov, “Distributed representations of sentences and documents,” Computer Science, vol. 4, pp. 1188–1196, 2014. [Google Scholar]
- C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines, “ in IEEE TRANS. NEURAL NETWORKS, 2002, pp. 415–425. [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.