Which of the following statements is true about the SVM algorithm?

Question

BlackTom AI · Accepted Answer

The question asks which statement is true about the SVM algorithm, but the provided data shows an empty list of answer options, which means there are no choices to evaluate.

Since there are no alternative statements to compare, I cannot assess each option for correctness or incorrectness in the usual way. Instead, I can analyze the single given statement and explain why it is generally considered false, which helps clarify typical misconceptions.

Statement to evaluate: 'It can be both a parametric and non-parametric model.'

First, consider the core nature of support vector machines (SVMs). In standard SVMs, the decision boundary is determined by a subset of the training data called support vectors. The number of these support vectors is not fixed in advance and can grow with the size and representational needs of the dataset. This characteristic aligns more with non-parametric behavior, where model complexity adapts to the data rather than being fixed by a predetermined number of parameters.

Additionally, the use of kernels (linear, polynomial, RBF, etc.) allows SVMs to operate in high-dimensional or even infinite-dimensional feature spaces without explicitly computing coordinates in those spaces. This kernel-based flexibility reinforces the view of SVMs as non-parametric in nature, since the model's capacity adjusts with the data through the selection of support vectors and kernel-induced feature space, rather than being determined by a fixed parameter count.

However, some people might loosely describe SVMs as 'parametric' in the sense that they depend on a finite set of parameters (e.g., support vector coefficients, bias term, and kernel parameters). This description is technically imprecise, because the effective number of parameters can scale with the data, and the decision boundary is not restricted to a fixed-dimensional parametric form independent of the dataset.

Now, addressing the broader context: because there are no other answer options provided, we cannot compare this statement to alternatives. If options existed, a correct analysis would involve identifying that the statement is misleading or incorrect in standard machine learning terminology, and explaining why.

In summary, the common and widely accepted view is that SVMs are non-parametric models due to data-dependent model complexity and the role of support vectors, even though some parameter-like elements exist. The given statement asserting that SVMs can be both parametric and non-parametric is generally considered false in the conventional sense.

Machine Learning: Fundamentals and Applications Quiz 7 SVM

Which of the following statements is true about the SVM algorithm?

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