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Tag: training
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Blob Taverna XPath practical slides .

Created: 2011-03-30 09:17:52 | Last updated: 2011-03-30 09:17:55

Credits: User Paul Fisher

License: Creative Commons Attribution-Share Alike 3.0 Unported License

Taverna training material for using and configuring XPath queries using native XPath Java methods, and XPath Service templates.

File type: PowerPoint presentation

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Blob Taverna Iteration training slides

Created: 2011-03-28 11:44:06 | Last updated: 2011-03-28 11:44:08

Credits: User Paul Fisher

License: Creative Commons Attribution-Share Alike 3.0 Unported License

Training slides to outline how to use iterations and iteration strategies in Taverna

File type: PowerPoint presentation

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Blob Bonn Taverna Training agenda March/April 2011

Created: 2011-03-28 11:31:38 | Last updated: 2011-03-28 11:31:39

Credits: User Paul Fisher

License: Creative Commons Attribution-Share Alike 3.0 Unported License

Agenda of Taverna training

File type: Word document

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Blob Taverna Introduction

Created: 2011-03-28 11:29:08 | Last updated: 2011-03-28 11:29:10

Credits: User Paul Fisher

License: Creative Commons Attribution-Share Alike 3.0 Unported License

This powerpoint presentation contains a set of starting exercises for begining your Taverna training.

File type: PowerPoint presentation

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Workflow Change Class Distribution of Your Training... (1)

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This example process shows how to change the class distribution of your training data set (in this case the training data is what ever comes out of the "myData reader"). The given training set has a distribution of 10 "Iris-setosa" examples, 40 "Iris-versicolor" examples and 50 "Iris-virginica" examples. The aim is to get a data set which has the class distribution for the label, lets say 10 "Iris-setosa", 20 "Iris-versicolor" and 20 "Iris-virginica. Beware that this may change some propert...

Created: 2011-01-21 | Last updated: 2011-01-21

Workflow 1. Getting Started: Learn and Store a Model (1)

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This getting started process shows the first step of learning and storing a model. After a model is learned, you can load (Retrieve operator) the model and apply it to a test data set (see 2. Getting Started: Retrieve and Apply Model). The process is NOT concerned with evaluation of the model. This process will not immediately run in RapidMiner because you have to adjust the repository path in the Retrieve operator. Tags: Rapidminer, model, learn, learning, training, train, store, first step

Created: 2011-01-17 | Last updated: 2011-01-17

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