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Kernel-based Discriminative Learning Algorithms for Labeling Structured Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kashima, Hisashi Tsuboi, Yuta |
| Copyright Year | 2004 |
| Abstract | We introduce a new perceptron-based discriminative learning algorithm for labeling structural data such as sequences, trees and graphs. Since it is fully kernelized and employs the pointwise label prediction, large features including arbitrary number of hidden variables can be incorporated with polynomial time complexity. This is contrasted with existing labelers that can handle only features of a small number of hidden variables such as Maximum Entropy Markov Models and Conditional Random Fields. We also introduce several kernel functions for labeling sequences, trees and graphs and the efficient algorithms for them. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://hkashima.github.io/publication/ai2004.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
