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PLEASD: A Matlab Toolbox for Structured Learning
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doc First time add PLEASD source code
references First time add PLEASD source code
README.txt
bmrm.m
bmrmGetGradient.m First time add PLEASD source code
bmrmGetLoss.m First time add PLEASD source code
bmrmUpdateModelL2.m First time add PLEASD source code
colsInCell.m
getField.m First time add PLEASD source code
perceptron.m First time add PLEASD source code
perceptronGetLoss.m First time add PLEASD source code
println.m First time add PLEASD source code
rowsInCell.m First time add PLEASD source code
slpaGetLinearUpperBound.m
slpa_bmrm.m First time add PLEASD source code
slpa_perceptron.m First time add PLEASD source code
trackingDemo.m First time add PLEASD source code
trackingGetFullLoss.m First time add PLEASD source code
trackingJointFeature.m First time add PLEASD source code
trackingLoss.m First time add PLEASD source code
trackingPredictor.m

README.txt

[About]
PLEASD: A Matlab Toolbox for Structured Learning
PLEASD stands for Prediction and LEArning for Structured Data. It is a Matlab toolbox of algorithmic frameworks for training structured prediction models. We provide this toolbox to ease the process of applying structured learning to new problems. We attempt to minimize users’ involvement in coding the structured learning framework such that they can focus on issues related to their specific problems. Currently, PLEASD has included the following structured learning frameworks:
1. Bundle method for risk minimization (type help bmrm);
2. Structured perceptron learning (type help perceptron);
3. Structured learning from partial annotations (type help slpa_bmrm);
4. Structured perceptron learning from partial annotations (type help slpa_perceptron).

[License]
(c) MIT License for worry-free use and distribution.

[Examples and User Guide]
Demo: see trackingDemo.m for examples.
User Guide: included in the package.

[Contact]
Please forward any suggestions, bug reports, questions to xinghua.lou@gmail.com. Your feedback is highly appreciated.
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