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
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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
