Andy Pack
efa7a84a8b
Affected files: .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json Languages/Spanish/Spanish.md STEM/AI/Classification/Classification.md STEM/AI/Classification/Decision Trees.md STEM/AI/Classification/Logistic Regression.md STEM/AI/Classification/Random Forest.md STEM/AI/Classification/Supervised/SVM.md STEM/AI/Classification/Supervised/Supervised.md STEM/AI/Neural Networks/Activation Functions.md STEM/AI/Neural Networks/CNN/CNN.md STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md STEM/AI/Neural Networks/CNN/GAN/GAN.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/Properties+Capabilities.md STEM/AI/Neural Networks/SLP/Perceptron Convergence.md
39 lines
1.0 KiB
Markdown
39 lines
1.0 KiB
Markdown
---
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tags:
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- ai
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- classification
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---
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*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/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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