Applying Automatic Differentiation and Truncated Newton Methods to Conditional Random Fields
Date Issued
2008
Date
2008
Author(s)
Wang, Hsiang-Jui
Abstract
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 second-order information like the Hessian-vector products may not be suitable. Automatic differentiation is a technique to evaluate derivatives of a function without its gradient function. Moreover, computing Hessian-vector products by automatic differentiation only requires the gradient function but not the Hessian matrix. This thesis first gives a study on the background knowledge of automatic differentiation. Then it merges truncated Newton methods with automatic differentiation for solving conditional random fields.
Subjects
automatic differentiation
conjugate gradient methods
truncated New- ton methods
maximum entropy
conditional random fields
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-97-R95922073-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):d3eebbed7d4a8fb1456440b3064d8e70