63 lines
1.7 KiB
Markdown
63 lines
1.7 KiB
Markdown
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# Supervised
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- Dataset with inputs manually annotated for desired output
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- Desired output = supervisory signal
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- Manually annotated = ground truth
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- Annotated correct categories
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## Split data
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- Training set
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- Test set
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***Don't test on training data***
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## Top-K Accuracy
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- Whether correct answer appears in the top-k results
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## Confusion Matrix
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Samples described by ***feature vector***
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Dataset forms a matrix
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# Un-Supervised
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- No example outputs given, learns how to categorise
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- No teacher or critic
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## Harder
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- Must identify relevant distinguishing features
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- Must decide on number of categories
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# Reinforcement Learning
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- No teacher - critic instead
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- Continued interaction with the environment
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- Minimise a scalar performance index
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- Critic
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- Converts primary reinforcement to heuristic reinforcement
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- Both scalar inputs
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- Delayed reinforcement
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- System observes temporal sequence of stimuli
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- Results in generation of heuristic reinforcement signal
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- Minimise cost-to-go function
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- Expectation of cumulative cost of actions taken over sequence of steps
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- Instead of just immediate cost
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- Earlier actions may have been good
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- Identify and feedback to environment
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- Closely related to dynamic programming
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## Difficulties
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- No teacher to provide desired response
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- Must solve temporal credit assignment problem
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- Need to know which actions were the good ones
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# Fitting
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- Over-fitting
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- Classifier too specific to training set
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- Can't adequately generalise
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- Under-fitting
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- Too general, not inferred enough detail
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- Learns non-discriminative or non-desired pattern
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# ROC
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Receiver Operator Characteristic Curve
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