Difference between revisions of "User:Francis Tyers/Perceptron"
Jump to navigation
Jump to search
m |
|||
| Line 1: | Line 1: | ||
{{TOCD}} |
{{TOCD}} |
||
| + | |||
| + | 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 <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. |
||
===Features=== |
===Features=== |
||
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 |
