stem/AI/Classification/Supervised
Andy Pack efa7a84a8b vault backup: 2023-12-27 21:56:22
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
2023-12-27 21:56:22 +00:00
..
README.md vault backup: 2023-06-07 09:02:27 2023-06-07 09:02:27 +01:00
Supervised.md vault backup: 2023-12-27 21:56:22 2023-12-27 21:56:22 +00:00
SVM.md vault backup: 2023-12-27 21:56:22 2023-12-27 21:56:22 +00:00

tags
ai
classification

Gaussian Classifier

  • With T labelled 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