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