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
609 B
609 B
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Gaussian Classifier
- With
Tlabelled data
q_t(i)=
\begin{cases}
1 & \text{if class } i \\
0 & \text{otherwise}
\end{cases}$$
- Indicator function
- Mean parameter
$$\hat m_i=\frac{\sum_tq_t(i)o_t}{\sum_tq_t(i)}$$
- Variance parameter
$$\hat v_i=\frac{\sum_tq_t(i)(o_t-\hat m_i)^2}{\sum_tq_t(i)}$$
- Distribution weight
- Class prior
- $P(N_i)$
$$\hat c_i=\frac 1 T \sum_tq_t(i)$$
$$\hat \mu_i=\frac{\sum_{t=1}^Tq_t(i)o_t}{\sum_{t=1}^Tq_t(i)}$$
$$\hat\sum_i=\frac{\sum_{t=1}^Tq_t(i)(o_t-\mu_i)(o_t-\mu_i)^T}{\sum_{t=1}^Tq_t(i)}$$
- For K-dimensional