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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 |
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| Classification.md | ||
| README.md | ||
| Supervised.md | ||
Given an observation, determine one class from a set of classes that best explains the observation
Features are discrete or continuous
- 2 category classifier
- Dichotomiser
Argmax
Argument that gives the maximum value from a target function
Gaussian Classifier
- Each class $i$ has it's own Gaussian
N_i=N(m_i,v_i)
\hat i=\text{argmax}_i\left(p(o_t|N_i)\cdot P(N_i)\right)\hat i=\text{argmax}_i\left(p(o_t|N_i)\right)- With equal priors
Discrete Classifier
- Each class
ihas it's own histogramH_i- Describes the probability of each observation type
k P(o_t=k|H_i), based on class-specific type counts
- Describes the probability of each observation type
\hat i=\text{argmax}_i\left(P(o_t=k|H_i)\right)- Nothing else known about classes
\hat i=\text{argmax}_i\left(P(o_t=k|H_i)\cdot P(H_i)\right)- Given class priors
P(H_i) - Maximum posterior probability
- Bayes
