An SVM is chosen for a clearly separable dataset. What does SVM aim to optimize?

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BlackTom AI · Accepted Answer

The prompt asks about an SVM applied to a clearly separable dataset and what objective it optimizes. 
Option 1: 'Reduce token count' is unrelated to SVMs and to the core objective of separating data; token count is a concept from language models or text processing, not margin optimization. Therefore this choice is incorrect. 
Option 2: 'Minimize entropy' corresponds to decision tree or information-theoretic criteria in some classifiers, but it does not describe the SVM goal of maximizing the separation margin between classes in the feature space. This makes it incorrect for SVMs. 
Option 3: 'Identify centroids' is a characteristic task of clustering algorithms like k-means, not of support vector machines; SVMs do not seek cluster centers, so this statement misattributes a clustering objective to SVM. Hence it is incorrect. 
Option 4: 'Maximize margin' captures the fundamental objective of support vector machines: to find the hyperplane that best separates the classes with the largest possible margin, i.e., the widest distance between the hyperplane and the nearest data points from each class. This is the correct description of what SVM aims to optimize for clearly separable data.

IS 4487-006 Fall 2025 Final Exam December 10 from 10:30 to 12:30

An SVM is chosen for a clearly separable dataset. What does SVM aim to optimize?

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