Difference between revisions of "User:Francis Tyers/Perceptron"
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A perceptron is a classifier that |
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The classifier consists of: |
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* Binary features |
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* Weights |
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==Example== |
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Here is a worked example of a perceptron applied to the task of lexical selection. Lexical selection is the task of choosing a target translation <code>t</code> for a given source word <code>s</code> in a context <code>C</code> out of a set of possible translations <code>T</code>. A perceptron makes a classification decision for a single class, so we need to train a separate perceptron for each possible target word selection. |
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===Features=== |
===Features=== |
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Revision as of 19:51, 8 November 2014
A perceptron is a classifier that
The classifier consists of:
- Binary features
- Weights
Example
Here is a worked example of a perceptron applied to the task of lexical selection. Lexical selection is the task of choosing a target translation t for a given source word s in a context C out of a set of possible translations T. A perceptron makes a classification decision for a single class, so we need to train a separate perceptron for each possible target word selection.
Features
Training data
| 1:sec | season |
| 1:de 2:el 3:any | season |
| 1:de 2:tren | station |
| 1:humit | season |
| 1:de 2:televisió | station |
| 1:de 2:línia | station |
| 1:plujós | season |
| 1:naval | station |
