Support Vector Machines (SVM)
Maximum Margin Hyperplanes, Soft Margins & The Dual Kernel Trick
#SVM#Kernels#Convex Optimization#Margin#RBF Kernel
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Prompting an LLM to classify medical genomics vectors (e.g. 20,000 gene expressions on 200 patient samples).
Why It Fails in Production:
LLMs cannot process thousands of continuous gene expression floats accurately; linear SVMs excel on wide datasets (d >> N).
Targeted Algorithm (Support Vector Machines (SVM))
Latency:0.02ms
Cost / 1M Ops:$0.00
Determinism:Convex Global Optimum
Generative LLM Alternative
Latency:3,000ms
Cost / 1M Ops:$12,000
Determinism:Prone to Hallucinations