University AI - Decision Boundary or Decision Rule

Decision Boundary or Decision Rule

Knowing the pdfs and conditional pdfs of different events, for example we can create those PDFs on the basis of data we have gathered, we define the decision rule as the major probability of all the events, given some information. For example, we take data on the distribution of males and females at different ours in an apartments for a month, we create many different conditional PDFs given all the information, and then we define the decision boundary as follows:

• $x$ in this case is the time variable.
• $F$ and $M$ are the condition of being female or male.
• $\theta$ is the decision boundary.

NOTE: $\theta$ is a function, this is a particular case of it being a constant.

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Feature Extraction

A good feature extraction process should be:

• in digital form
• rich (more information as possible)
• simple (as small dimension as possible)

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Original Files

~Ex.:

• $p(x|F)$ : Probability that $x$ (the person knocking on our door) is female.
• $p(x|M)$ : Probability that $x$ is male.
• $\theta$ : decision boundary.

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