Let x, q E R be two feature vectors, and let K (x, q) = (x...
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Let x, q E R² be two feature vectors, and let K (x, q) = (x¹ q + 1)². This is often known as a polynomial kernel. It's simple to compute: you just take the dot product between two feature vectors, add one, and then square the result. But what kind of feature mapping does this kernel implicitly use? Assuming we can write K (x, q) = (x) (q), derive an expression for (x). Enter the solution as a vector (x) = [ƒ1 (x₁, x₂),, fN (x1, x₂)]. φ(x) = Let x, q E R² be two feature vectors, and let K (x, q) = (x¹ q + 1)². This is often known as a polynomial kernel. It's simple to compute: you just take the dot product between two feature vectors, add one, and then square the result. But what kind of feature mapping does this kernel implicitly use? Assuming we can write K (x, q) = (x) (q), derive an expression for (x). Enter the solution as a vector (x) = [ƒ1 (x₁, x₂),, fN (x1, x₂)]. φ(x) =
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Related Book For
A Survey of Mathematics with Applications
ISBN: 978-0134112107
10th edition
Authors: Allen R. Angel, Christine D. Abbott, Dennis Runde
Posted Date:
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