3) Assumer we are given with following kernel function: k (x, xª) = (1 + x¹ x)² for kernelized perceptron. Assume you are given two positive points of x₁ = and x₂ = [2] and two negative points of x3 = H] and 4 = Also assume that the [4] trained parameters are: a₁ = 2, α₂ = 1, a3 = -2, a4 = -1 for each data point and b = 0.1. Find the output of classification for the unseen point x = "
3) Assumer we are given with following kernel function: k (x, xª) = (1 + x¹ x)² for kernelized perceptron. Assume you are given two positive points of x₁ = and x₂ = [2] and two negative points of x3 = H] and 4 = Also assume that the [4] trained parameters are: a₁ = 2, α₂ = 1, a3 = -2, a4 = -1 for each data point and b = 0.1. Find the output of classification for the unseen point x = "
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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![3) Assumer we are given with following kernel function: k (x, x¹) = (1 + x¹x²)² for kernelized perceptron. Assume you are given two
[2]
[7]
[4]
Also assume that the
-1 for each data point and b = 0.1. Find the output of classification for the
positive points of 1 =
trained parameters are: a₁
[3]
unseen point x =
and x2
=
= 2,02
=
=
H
:1, a3
=
and two negative points of 3
-2, α4
=
and 4 =](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb1144e19-745d-4345-9211-16ab01627c67%2F13a1cff2-25ae-45ea-bc62-f893730b26d0%2Fjgnp4rg_processed.png&w=3840&q=75)
Transcribed Image Text:3) Assumer we are given with following kernel function: k (x, x¹) = (1 + x¹x²)² for kernelized perceptron. Assume you are given two
[2]
[7]
[4]
Also assume that the
-1 for each data point and b = 0.1. Find the output of classification for the
positive points of 1 =
trained parameters are: a₁
[3]
unseen point x =
and x2
=
= 2,02
=
=
H
:1, a3
=
and two negative points of 3
-2, α4
=
and 4 =
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