Abstract
We would like to start this book with an ancient story about “The Blind Men and the Elephant” from John Godfrey Saxe. This story is a famous Indian fable about six blind sojourners who come across different parts of an elephant in their life journeys. In turn, each blind man creates his own version of reality from that limited experiences and perspectives. Instead of explaining its philosophical meanings, we indent to use this story to illustrate the current situations that both the academia and industry are facing about artificial intelligence, machine learning, and data mining.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
E. Arisoy, T. Sainath, B. Kingsbury, B. Ramabhadran, Deep neural network language models, in Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT (WLM ’12) (Association for Computational Linguistics, Stroudsburg, 2012), pp. 20–28
A. Bordes, N. Usunier, A. Garcia-Durán, J. Weston, O. Yakhnenko, Translating embeddings for modeling multi-relational data, in Advances in Neural Information Processing Systems (2013)
L. Breiman, Bagging predictors. Mach. Learn. 24(2), 123–40 (1996)
R. Caruana, Multitask learning. Mach. Learn. 28(1), 41–75 (1997)
W. Dai, Q. Yang, G. Xue, Y. Yu, Boosting for transfer learning, in Proceedings of the 24th International Conference on Machine Learning (ACM, New York, 2007), pp. 193–200
L. Deng, G. Hinton, B. Kingsbury, New types of deep neural network learning for speech recognition and related applications: an overview, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (IEEE, Piscataway, 2013)
S. Fortunato, Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)
Y. Freund, R. Schapire, A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(5), 771–780 (1999)
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning (MIT Press, Cambridge, 2016). http://www.deeplearningbook.org
D. Gruhl, R. Guha, D. Liben-Nowell, A. Tomkins, Information diffusion through blogspace, in Proceedings of the 13th International Conference on World Wide Web (ACM, New York, 2004), pp. 491–501
S. Hill, Elite and upper-class families, in Families: A Social Class Perspective (2012)
G. Hinton, S. Osindero, Y. Teh, A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, B. Kingsbury, Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process. Mag. 29(6), 82–97 (2012)
H. Jaeger, Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach, Technical report (2002)
S. Jin, J. Zhang, P. Yu, S. Yang, A. Li, Synergistic partitioning in multiple large scale social networks, in 2014 IEEE International Conference on Big Data (Big Data) (IEEE, Piscataway, 2014)
D. Kempe, J. Kleinberg, É Tardos, Maximizing the spread of influence through a social network, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2003), pp. 137–146
Y. Kim, Convolutional neural networks for sentence classification, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Association for Computational Linguistics, 2014), pp. 1746–1751
X. Kong, J. Zhang, P. Yu, Inferring anchor links across multiple heterogeneous social networks, in Proceedings of the 22nd ACM international conference on Information and Knowledge Management (ACM, New York, 2013), pp. 179–188
A. Krizhevsky, I. Sutskever, G. Hinton, Imagenet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012)
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015). http://dx.doi.org/10.1038/nature14539
D. Liben-Nowell, J. Kleinberg, The link prediction problem for social networks, in Proceedings of the Twelfth International Conference on Information and Knowledge Management (ACM, New York, 2003), pp. 556–559
R. Maclin, D. Opitz, Popular ensemble methods: an empirical study. J. Artif. Intell. Res. (2011). arXiv:1106.0257
A. Mnih, G. Hinton, A scalable hierarchical distributed language model, in Advances in Neural Information Processing Systems 21 (NIPS 2008) (2009)
H. Nam, B. Han, Learning multi-domain convolutional neural networks for visual tracking. Comput. Vis. Pattern Recognit. (2015). arXiv:1510.07945
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, A. Ng, Multimodal deep learning, in Proceedings of the 28th International Conference on Machine Learning (ICML-11) (2011), pp. 689–696
S. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
B. Perozzi, R. Al-Rfou, S. Skiena, Deepwalk: online learning of social representations, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2014), pp. 701–710
R. Polikar, Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)
R. Salakhutdinov, G. Hinton, Semantic hashing. Int. J. Approx. Reason. 50(7), 969–978 (2009)
S. Thrun, Lifelong Learning Algorithms (1998)
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P. Manzagol, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
J. Weston, S. Bengio, N. Usunier, Large scale image annotation: learning to rank with joint word-image embeddings. J. Mach. Learn. Res. 81(1), 21–35 (2010)
J. Weston, S. Bengio, N. Usunier, Wsabie: scaling up to large vocabulary image annotation, in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (2011)
D. Wolpert, Stacked generalization. Neural Netw. 5, 241–259 (1992)
T. Xiao, H. Li, W. Ouyang, X. Wang, Learning deep feature representations with domain guided dropout for person re-identification. Comput. Vis. Pattern Recognit. (2016). arXiv:1604.07528
C. Xu, D. Tao, C. Xu, A survey on multi-view learning. Mach. Learn. (2013). arXiv:1304.5634
Q. Zhan, J. Zhang, S. Wang, P. Yu, J. Xie, Influence maximization across partially aligned heterogeneous social networks, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (Springer, Berlin, 2015), pp. 58–69
Q. Zhan, J. Zhang, X. Pan, M. Li, P. Yu, Discover tipping users for cross network influencing, in 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI) (IEEE, Piscataway, 2016)
J. Zhang, Social network fusion and mining: a survey. Soc. Inf. Netw. (2018). arXiv:1804.09874
J. Zhang, P. Yu, Community detection for emerging networks, in Proceedings of the 2015 SIAM International Conference on Data Mining (Society for Industrial and Applied Mathematics, Philadelphia, 2015)
J. Zhang, P. Yu, MCD: mutual clustering across multiple social networks, in 2015 IEEE International Congress on Big Data (IEEE, Piscataway, 2015)
J. Zhang, P. Yu, Multiple anonymized social networks alignment, in 2015 IEEE International Conference on Data Mining (IEEE, Piscataway, 2015)
J. Zhang, P. Yu, PCT: partial co-alignment of social networks, in Proceedings of the 25th International Conference on World Wide Web (ACM, New York, 2016), pp. 749–759
J. Zhang, X. Kong, P. Yu, Predicting social links for new users across aligned heterogeneous social networks, in 2013 IEEE 13th International Conference on Data Mining (IEEE, Piscataway, 2013)
J. Zhang, X. Kong, P. Yu, Transferring heterogeneous links across location-based social networks, in Proceedings of the 7th ACM International Conference on Web Search and Data Mining (ACM, New York, 2014), pp. 303–312
J. Zhang, P. Yu, Z. Zhou, Meta-path based multi-network collective link prediction, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2014), pp. 1286–1295
J. Zhang, W. Shao, S. Wang, X. Kong, P. Yu, PNA: partial network alignment with generic stable matching, in 2015 IEEE International Conference on Information Reuse and Integration (IEEE, Piscataway, 2015)
J. Zhang, S. Wang, Q. Zhan, P. Yu, Intertwined viral marketing in social networks, in 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (IEEE, Piscataway, 2016)
J. Zhang, P. Yu, Y. Lv, Q. Zhan, Information diffusion at workplace, in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (ACM, New York, 2016), pp. 1673–1682
J. Zhang, Q. Zhan, P. Yu, Concurrent alignment of multiple anonymized social networks with generic stable matching, in Theoretical Information Reuse and Integration (Springer, Cham, 2016), pp. 173–196
J. Zhang, J. Chen, J. Zhu, Y. Chang, P. Yu, Link prediction with cardinality constraints, in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (ACM, New York, 2017), pp. 121–130
J. Zhang, C. Xia, C. Zhang, L. Cui, Y. Fu, P. Yu, BL-MNE: emerging heterogeneous social network embedding through broad learning with aligned autoencoder, in Proceedings of the 2017 IEEE International Conference on Data Mining (IEEE, Piscataway, 2017)
J. Zhang, J. Chen, S. Zhi, Y. Chang, P. Yu, J. Han, Link prediction across aligned networks with sparse and low rank matrix estimation, in 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (IEEE, Piscataway, 2017)
J. Zhang, L. Cui, P. Yu, Y. Lv, BL-ECD: broad learning based enterprise community detection via hierarchical structure fusion, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (ACM, New York, 2017), pp. 859–868
Z. Zhou, Ensemble Methods: Foundations and Algorithms, 1st edn. (Chapman & Hall/CRC, London, 2012)
J. Zhu, J. Zhang, L. He, Q. Wu, B. Zhou, C. Zhang, P. Yu, Broad learning based multi-source collaborative recommendation, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (ACM, New York, 2017), pp. 1409–1418
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Zhang, J., Yu, P.S. (2019). Broad Learning Introduction. In: Broad Learning Through Fusions. Springer, Cham. https://doi.org/10.1007/978-3-030-12528-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-12528-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-12527-1
Online ISBN: 978-3-030-12528-8
eBook Packages: Computer ScienceComputer Science (R0)