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This is a simple (and parallelizable) tuning method for cdec
which enables training weights of very many (sparse) features
on the full training set.
Please cite as:
> "Joint Feature Selection in Distributed Stochastic
> Learning for Large-Scale Discriminative Training in
> SMT" (Simianer, Riezler, Dyer; ACL 2012)
>
Building
--------
Builds when building cdec, see ../../BUILDING .
Running
-------
Download examples for all use cases from [1] and extract here.
TODO
----
* "stop_after" stop after X inputs
* "select_weights" average, best, last
* "rescale" rescale weight vector
* implement SVM objective?
* other variants of l1 regularization?
* l2 regularization?
* l1/l2 regularization?
* scale updates by bleu difference
* AdaGrad, per-coordinate learning rates
* batch update
* "repeat" iterate over k-best lists
* show k-best loss improvement
* "quiet"
* "verbose"
* fix output
Legal
-----
Copyright (c) 2012-2016 by Patrick Simianer <p@simianer.de>
See the file LICENSE.txt in the root folder for the licensing terms that this
software is released under.
[1] http://simianer.de/dtrain-example.tar.xz
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