3) Assumer we are given with following kernel function: k (x, x¹) = (1 + x²x)² for kernelized perceptron. Assume you ar positive points of #1 = [] and 22 = [2], and two negative points of 23 =[²] and TA -[²]. .Also assume trained parameters are: a₁ = 2, a₂ = 1, a3 = -2, a4 = -1 for each data point and b = 0.1. Find the output of classificat unseen point x = 3
3) Assumer we are given with following kernel function: k (x, x¹) = (1 + x²x)² for kernelized perceptron. Assume you ar positive points of #1 = [] and 22 = [2], and two negative points of 23 =[²] and TA -[²]. .Also assume trained parameters are: a₁ = 2, a₂ = 1, a3 = -2, a4 = -1 for each data point and b = 0.1. Find the output of classificat unseen point x = 3
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![3) Assumer we are given with following kernel function: k (x, x¹) = (1 + x ²)² for kernelized perceptron. Assume you are given two
2
[]
H
[]
and 4 =
. Also assume that the
trained parameters are: a₁ = 2, a2 = 1, a3 = -2, a4 = -1 for each data point and b = 0.1. Find the output of classification for the
-3
unseen point x =
positive points of x₁ =
and x₂ =
,
and two negative points of x3 =](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fb1144e19-745d-4345-9211-16ab01627c67%2F41f1b1b7-8c22-446d-8b25-da5cd69ca833%2Fqsnp85_processed.png&w=3840&q=75)
Transcribed Image Text:3) Assumer we are given with following kernel function: k (x, x¹) = (1 + x ²)² for kernelized perceptron. Assume you are given two
2
[]
H
[]
and 4 =
. Also assume that the
trained parameters are: a₁ = 2, a2 = 1, a3 = -2, a4 = -1 for each data point and b = 0.1. Find the output of classification for the
-3
unseen point x =
positive points of x₁ =
and x₂ =
,
and two negative points of x3 =
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