It may seem like a very basic question to the machine-learning fellows out there... but: I'm writing a paper on a computer-vision algorithm that identifies various events in time-lapse images of biological tissues. Among other things, this algorithm has a binary weighted-KNN (wKNN) classification step. The way I implemented it is the following:
Assume an initial dataset of many unlabeled examples,
- Label manually several 'true' and several 'false' examples.
- Let the wKNN classifier classify the rest of the dataset.
- In case of need - correct the labeling of the classifier by manually labeling several erroneously labeled examples (i.e. add these examples to the training set), and call the wKNN classifier again with the updated training set (i.e. go back to step #2).
Importantly, it's expected that every time / most of the time when I'll classify new data with this classifier, there will be some corrections to make (i.e. updates of the training set). This is contrary to situations in which you train the classifier once and just apply it on new data without trying to improve it.
The question is: is there a keyword / a name to this iterative / neverending process I could use in the paper that would save me these 10 lines of explanation and make it easier to people from the field?