Manifold Learning based on Bayesian Sequential Partitioning
碩士 === 國立交通大學 === 電子研究所 === 106 === The more complicated features used to construct a model, the more data we need for model learning. Unfortunately, the required amount of data grows exponentially as the feature space dimension grows. To deal with high-dimensional feature space, dimension reduction...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2017
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| Online Access: | http://ndltd.ncl.edu.tw/handle/nbkh34 |