Abstract
Machine learning is one field of Artificial Intelligence (AI) to help machines solve problems. Support Vector Machines (SVMs) are classic methods in machine learning field and are also used in many other AI fields. However, the model training is very time-consuming when meeting large scale data sets. Some efforts have been devoted to develop it for distributed memory clusters. Their bottleneck is the training phase, where the structure is immobile. In this paper, we propose Multi-Modes Cascade SVMs (MMCascadeSVMs) to adaptively reshape the structure. MMCascadeSVMs employs analytical hierarchy process to qualitatively analyse the similarity between adjacent hierarchies. Furthermore, MMCascadeSVMs leverages a two-stage algorithm: the first stage is to compute the similarity between two adjacent models, and the similarity is built for halting criterion. The second stage is to predict new samples based on multi models. MMCascadeSVMs can modify the structure of SVMs in distributed systems and reduce training time. Experiments show that our approach significantly reduces the total computation cost.
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Acknowledgment
The work was supported by the National Basic Research Program of China (project No. 2014CB340303) and the National Natural Science Foundation of China (project No. 61402514).
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Cui, L., Wang, C., Li, W., Tan, L., Peng, Y. (2017). Multi-Modes Cascade SVMs: Fast Support Vector Machines in Distributed System. In: Kim, K., Joukov, N. (eds) Information Science and Applications 2017. ICISA 2017. Lecture Notes in Electrical Engineering, vol 424. Springer, Singapore. https://doi.org/10.1007/978-981-10-4154-9_51
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DOI: https://doi.org/10.1007/978-981-10-4154-9_51
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