Applying Automatic Differentiation and Truncated Newton Methods to Conditional Random Fields
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 96 === In recent years, labeling sequential data arises in many fields. Conditional random fields are a popular model for solving this type of problems. Its Hessian matrix in a closed form is not easy to derive. This difficulty causes that optimization methods using se...
| Main Authors: | , |
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| Format: | Others |
| Language: | en_US |
| Published: |
2008
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| Online Access: | http://ndltd.ncl.edu.tw/handle/68481133280783589228 |