andy
1c441487f9
Affected files: .obsidian/workspace-mobile.json .obsidian/workspace.json Lab/Scratch Domain.md Money/Econ.md STEM/AI/Classification/Classification.md STEM/AI/Classification/README.md STEM/AI/Classification/Supervised.md STEM/AI/Neural Networks/CNN/Examples.md STEM/AI/Neural Networks/CNN/FCN/FCN.md STEM/AI/Neural Networks/CNN/FCN/FlowNet.md STEM/AI/Neural Networks/CV/Filters.md STEM/img/coordinate-change.png STEM/img/gaussian-class.png Tattoo/Engineering.md Want.md
34 lines
973 B
Markdown
34 lines
973 B
Markdown
*Given an observation, determine one class from a set of classes that best explains the observation*
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***Features are discrete or continuous***
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- 2 category classifier
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- Dichotomiser
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# Argmax
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Argument that gives the maximum value from a target function
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# Gaussian Classifier
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[Training](Supervised.md)
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- Each class $i$ has it's own Gaussian $N_i=N(m_i,v_i)$
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$$\hat i=\text{argmax}_i\left(p(o_t|N_i)\cdot P(N_i)\right)$$
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$$\hat i=\text{argmax}_i\left(p(o_t|N_i)\right)$$
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- With equal priors
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# Discrete Classifier
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- Each class $i$ has it's own histogram $H_i$
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- Describes the probability of each observation type $k$
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- $P(o_t=k|H_i)$, based on class-specific type counts
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$$\hat i=\text{argmax}_i\left(P(o_t=k|H_i)\right)$$
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- Nothing else known about classes
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$$\hat i=\text{argmax}_i\left(P(o_t=k|H_i)\cdot P(H_i)\right)$$
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- Given class priors $P(H_i)$
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- Maximum posterior probability
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- Bayes
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