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Items tagged with "training" (10)

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Groups (4)
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Network-member Ondex Tutorial

Unique name: OndexTutorial
Created: Monday 05 January 2009 @ 12:43:42 (GMT)

This group is provided for the Ondex training event (held at Manchester - Janurary 2009).

8 shared items   |   0 announcements

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Owner

Network-member Taverna 1 Tutorial

Unique name: taverna1tutorial
Created: Wednesday 14 January 2009 @ 15:10:18 (GMT)

This group was created for anyone who is currently undergoing Taverna 1 training. Please feel free to add your workflows, packs, and files as you wish.

0 shared items   |   0 announcements

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Owner

Network-member eLicoTutorial

Unique name: eLicoTutorial
Created: Wednesday 10 March 2010 @ 16:41:48 (GMT)

This group was created for the March 2010  eLico Taverna Training day, in Toulouse.

4 shared items   |   0 announcements

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Network-member RapidMiner Demo

Unique name: rapidminer_demo
Created: Saturday 01 May 2010 @ 09:49:28 (GMT)

This group is created for demo processes used in RapidMiner documentation, manual, or training. Feel free to add workflows if you consider them instructive examples.

5 shared items   |   1 announcements

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Latest announcement:: RCOMM 2011

Files (4)
Uploader

Blob Taverna Introduction

Created: 28/03/11 @ 11:29:08 | Last updated: 28/03/11 @ 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

Rating: 0.0 / 5 (0 ratings) | Comments: 0 | Viewed: 34 times | Downloaded: 55 times

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Blob Bonn Taverna Training agenda March/April 2011

Created: 28/03/11 @ 11:31:38 | Last updated: 28/03/11 @ 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

Rating: 0.0 / 5 (0 ratings) | Comments: 0 | Viewed: 20 times | Downloaded: 17 times

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Uploader

Blob Taverna Iteration training slides

Created: 28/03/11 @ 11:44:06 | Last updated: 28/03/11 @ 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

Rating: 0.0 / 5 (0 ratings) | Comments: 0 | Viewed: 20 times | Downloaded: 24 times

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Uploader

Blob Taverna XPath practical slides .

Created: 30/03/11 @ 09:17:52 | Last updated: 30/03/11 @ 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

Rating: 0.0 / 5 (0 ratings) | Comments: 0 | Viewed: 11 times | Downloaded: 13 times

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Workflows (2)
Original Uploader

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

Created: 17/01/11 @ 08:40:34 | Last updated: 17/01/11 @ 08:56:57

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

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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

Rating: 0.0 / 5 (0 ratings) | Versions: 1 | Reviews: 0 | Comments: 0 | Citations: 0

Viewed: 101 times | Downloaded: 38 times

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Original Uploader

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

Created: 21/01/11 @ 14:57:11 | Last updated: 21/01/11 @ 14:57:12

License: Creative Commons Attribution-No Derivative Works 3.0 Unported License

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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...

Rating: 0.0 / 5 (0 ratings) | Versions: 1 | Reviews: 0 | Comments: 0 | Citations: 0

Viewed: 85 times | Downloaded: 37 times

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