Knowledge Intensive Machine Learning, Part 1
Machine learning is all about learning from extensional information (datapoints). The incorporation of intensional information (domain knowledge) is generally done by hand via data encoding and feature and model selection. Doing this well is arguably the most difficult part of a successful machine learning system. It would sure be nice if our learning algorithm could make direct use of qualitative domain knowledge, say for model or feature selection, or reducing the parameter space? This is the idea behind "knowledge intensive machine learning".
Probably the best (though almost trivial) example of this is the simple Bayesian network. A Bayes net is a probabilistic model which can be learned from data, but for which the user can very easily specify high-level qualitative domain knowledge (causality or conditional independence) which significantly constrains the parameter space. What else do we have? Not much.
Here's a motivating example: suppose you are building a simple anti-smoking propaganda Bayes net. You have domain knowledge that tells you what nodes should be connected (for example, smoking and lung cancer). But we know more than that structure: the more a person smokes the more at risk they are for lung cancer. The qualitative reasoning folks would write this as "SmokingFrequency Q+ LungCancer", indicating a qualitative (perhaps stochastic) monotonic influence. Now, instead of using that statement for qualitative simulation, we use it for putting a prior on our CPTs that enforces stochastic monotonicity, resulting in better classifiers in low-data situations.
Probably the best (though almost trivial) example of this is the simple Bayesian network. A Bayes net is a probabilistic model which can be learned from data, but for which the user can very easily specify high-level qualitative domain knowledge (causality or conditional independence) which significantly constrains the parameter space. What else do we have? Not much.
Here's a motivating example: suppose you are building a simple anti-smoking propaganda Bayes net. You have domain knowledge that tells you what nodes should be connected (for example, smoking and lung cancer). But we know more than that structure: the more a person smokes the more at risk they are for lung cancer. The qualitative reasoning folks would write this as "SmokingFrequency Q+ LungCancer", indicating a qualitative (perhaps stochastic) monotonic influence. Now, instead of using that statement for qualitative simulation, we use it for putting a prior on our CPTs that enforces stochastic monotonicity, resulting in better classifiers in low-data situations.
posted by E-Rock at
7:15 PM
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