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02_​Active_​Learning_​PBCA_​modular_​Score

Active Learning with Modular Score

This workflow shows an example of Active Learning. We read a simple dataset of images separated in two classes and calculate some features on them. Now the Active Learning Loop determines the best sample which could be manuallay labeled by a user and benefits most to the separation of the calsses. The decision of the best sample is based on a specific score. Here we use a modular score calculation approach in order to find the best sample.

Active Learning with Modular Score Press the green double arrow above to run the complete workflow. You may have toscroll down to see all contents of this tutorial. To see the contents of a meta node(grey nodes) you can double-click on it, which opens a new tab with the nodescontained in this meta node.Workflow DescriptionThis workflow shows an example of Active Learning. We read a simple dataset ofimages separated in two classes and calculate some features on them. Now theActive Learning Loop determines the best sample which could be manuallay labeledby a user and benefits most to the separation of the calsses. The decision of thebest sample is based on a specific score. Here we use a modular score calculationapproach in order to find the best sample.If you need more information about other topics, maybe our tutorials. For details see:https://tech.knime.org/book/knime-active-learning Read and prepare inputimages. Right-Click >View: Viewto open the annotation view ofthe node. The node needs to beexecuting.Active LearnLoop Start Data Input Element Selector Active LearnLoop End Modular ScoreCalculation Active Learning with Modular Score Press the green double arrow above to run the complete workflow. You may have toscroll down to see all contents of this tutorial. To see the contents of a meta node(grey nodes) you can double-click on it, which opens a new tab with the nodescontained in this meta node.Workflow DescriptionThis workflow shows an example of Active Learning. We read a simple dataset ofimages separated in two classes and calculate some features on them. Now theActive Learning Loop determines the best sample which could be manuallay labeledby a user and benefits most to the separation of the calsses. The decision of thebest sample is based on a specific score. Here we use a modular score calculationapproach in order to find the best sample.If you need more information about other topics, maybe our tutorials. For details see:https://tech.knime.org/book/knime-active-learning Read and prepare inputimages. Right-Click >View: Viewto open the annotation view ofthe node. The node needs to beexecuting.Active LearnLoop Start Data Input Element Selector Active LearnLoop End Modular ScoreCalculation

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: David Kolb
Created at: 2015-12-01
On NodePit since: 2019-07-12
Last update: 2026-07-15
Created with KNIME version: v3.7.1

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