andy
1513f2b378
Affected files: STEM/AI/Classification/Classification.md STEM/AI/Classification/Decision Trees.md STEM/AI/Classification/Gradient Boosting Machine.md STEM/AI/Classification/Logistic Regression.md STEM/AI/Classification/Random Forest.md STEM/AI/Classification/Supervised.md STEM/AI/Classification/Supervised/README.md STEM/AI/Classification/Supervised/SVM.md STEM/AI/Classification/Supervised/Supervised.md STEM/AI/Learning.md STEM/AI/Neural Networks/Learning/Boltzmann.md STEM/AI/Neural Networks/Learning/Competitive Learning.md STEM/AI/Neural Networks/Learning/Credit-Assignment Problem.md STEM/AI/Neural Networks/Learning/Hebbian.md STEM/AI/Neural Networks/Learning/Learning.md STEM/AI/Neural Networks/Learning/README.md STEM/AI/Neural Networks/RNN/Autoencoder.md STEM/AI/Neural Networks/RNN/Deep Image Prior.md STEM/AI/Neural Networks/RNN/MoCo.md STEM/AI/Neural Networks/RNN/Representation Learning.md STEM/AI/Neural Networks/RNN/SimCLR.md STEM/img/comp-learning.png STEM/img/competitive-geometric.png STEM/img/confusion-matrix.png STEM/img/decision-tree.png STEM/img/deep-image-prior-arch.png STEM/img/deep-image-prior-results.png STEM/img/hebb-learning.png STEM/img/moco.png STEM/img/receiver-operator-curve.png STEM/img/reinforcement-learning.png STEM/img/rnn+autoencoder-variational.png STEM/img/rnn+autoencoder.png STEM/img/simclr.png STEM/img/sup-representation-learning.png STEM/img/svm-c.png STEM/img/svm-non-linear-project.png STEM/img/svm-non-linear-separated.png STEM/img/svm-non-linear.png STEM/img/svm-optimal-plane.png STEM/img/svm.png STEM/img/unsup-representation-learning.png
23 lines
569 B
Markdown
23 lines
569 B
Markdown
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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 |