Active Learning PBCA 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 PBCA modular Score



EXAMPLES Server: 04_Analytics/12_Active_Learning/02_Active_Learning_PBCA_modular_Score04_Analytics/12_Active_Learning/02_Active_Learning_PBCA_modular_Score*
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* Find more about the Examples Server here.
The link will open the workflow directly in KNIME Analytics Platform (requirements: Windows; KNIME Analytics Platform must be installed with the Installer version 3.2.0 or higher). In other cases, please use the link to a zip-archive or open the provided path manually