Difference between revisions of "Daume and Marcu 2005 Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction"
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The authors present the Learning as Search Optimization (LaSO) framework. The algorithm is basically SEARN but analyzed differently (and also ~24 pages shorter). | The authors present the Learning as Search Optimization (LaSO) framework. The algorithm is basically SEARN but analyzed differently (and also ~24 pages shorter). | ||
| − | Like SEARN, LaSO attempts to combine the learning of the model with the search that occurs during decoding. | + | Like SEARN, LaSO attempts to combine the learning of the model with the search that occurs during decoding. Instead of learning the model, and then doing a search during decoding to find the best output <math>hat{y}</math> |
| + | |||
| + | <math>hat{y}=text{arg}\max_{y \in \mathcal{Y}} f(x,y;w)</math> | ||
=== Method === | === Method === | ||
| + | |||
| + | The generic search (decoding) algorithm is shown below: | ||
| + | |||
| + | [[File:LaSO Generic Search.png]] | ||
| + | |||
| + | The ''enqueue'' function puts the nodes onto the queue in some order. Depending on the order that the ''enqueue'' function puts nodes on the queue, you can get depth-first, breadth-first, beam, heuristic, A*, etc search algorithms from standard AI textbooks. The thing that is different about LaSO is the function ''enqueue''. | ||
| + | |||
| + | LaSO works by learning the ''enqueue'' function from the training examples. | ||
[[File:LaSO Algorithm.png]] | [[File:LaSO Algorithm.png]] | ||
Revision as of 03:04, 1 October 2011
Citation and Online Link
Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction An alternative formal analysis of Searn.
Summary
The authors present the Learning as Search Optimization (LaSO) framework. The algorithm is basically SEARN but analyzed differently (and also ~24 pages shorter).
Like SEARN, LaSO attempts to combine the learning of the model with the search that occurs during decoding. Instead of learning the model, and then doing a search during decoding to find the best output
Method
The generic search (decoding) algorithm is shown below:
The enqueue function puts the nodes onto the queue in some order. Depending on the order that the enqueue function puts nodes on the queue, you can get depth-first, breadth-first, beam, heuristic, A*, etc search algorithms from standard AI textbooks. The thing that is different about LaSO is the function enqueue.
LaSO works by learning the enqueue function from the training examples.
Experimental Result
Related Papers
In progress by User:Jmflanig
