One popular use case for SVM is text classification. SVM can be used to classify text documents into predefined categories, such as sentiment analysis (positive, negative, or neutral) or topic classification (sports, politics, entertainment, etc.). SVMs are particularly useful when dealing with high-dimensional data like text, where the number of features can be significantly larger than the number of training samples. By representing text documents in a numerical feature space, SVMs can effectively separate different classes and achieve high accuracy in classification.
SVM?
No idea. 😂
Some science journal text I copied from liminal
It's just a test of the event kind displaying correctly.
Chat GPT generated articles 😅. Made multiple articles to see if I could compare individual notes or mess around with hyperlinking:
```
articleTitles =[ topic , topic 2...]
toc = [what is it? , who would use it? ..., conclusion]
for article in articleTitles:
for section in toc:
AI. prompt("you are a blogger with skills in {topic}. You are currently writing {section}. Please write it in markdown.")
```
SVM = Support Vector Machine - "find me a 'curve' that separates these datapoints into two classes given their labels"
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