andy
b24f551589
Affected files: .obsidian/backlink.json .obsidian/graph.json .obsidian/workspace-mobile.json .obsidian/workspace.json Events/🪣🪣🪣.md Health/ADHD.md STEM/AI/Classification/Gradient Boosting Machine.md STEM/AI/Neural Networks/CV/Visual Search/Visual Search.md STEM/AI/Neural Networks/Learning/Tasks.md STEM/AI/Pattern Matching/Dynamic Time Warping.md STEM/AI/Problem Solving.md STEM/CS/Regex.md STEM/img/dtw-graph-unit.png STEM/img/dtw-graph.png STEM/img/dtw-gross-partitioning.png STEM/img/dtw-heatmap-distortion.png STEM/img/dtw-heatmap.png STEM/img/dtw-possible-movements.png STEM/img/dtw-score-pruning.png STEM/img/nn-tasks-function-approx-inverse.png STEM/img/nn-tasks-function-approx.png STEM/img/nn-tasks-pattern.png STEM/img/problem-solving-arch.png STEM/img/problem-solving-goal-based.png STEM/img/problem-solving-reflex.png STEM/img/visual-search-arch.png STEM/img/visual-search-crude.png |
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| .. | ||
| Supervised | ||
| Classification.md | ||
| Decision Trees.md | ||
| Gradient Boosting Machine.md | ||
| Logistic Regression.md | ||
| Random Forest.md | ||
| README.md | ||
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
