Spis treści

Dane Grid8x8 - Wizualizacja QCP - Jedna klasa vs. reszta

Klasa 1

G(x)= σ(x+β)(1-σ(x-β))
β=0.3
learningRate = 0.1
maxIterations = 1000
initiations = 10
eps = 0.001
lambda = 1.0

Klasa 2

G(x)= σ(x+β)(1-σ(x-β))
β=0.3
learningRate = 0.1
maxIterations = 1000
initiations = 10
eps = 0.001
lambda = 1.0

Klasa 3

G(x)= σ(x+β)(1-σ(x-β))
β=0.3
learningRate = 0.1
maxIterations = 1000
initiations = 10
eps = 0.001
lambda = 1.0

Klasa 4

G(x)= σ(x+β)(1-σ(x-β))
β=0.3
learningRate = 0.1
maxIterations = 1000
initiations = 10
eps = 0.001
lambda = 1.0

Klasa 5

G(x)= σ(x+β)(1-σ(x-β))
β=0.3
learningRate = 0.1
maxIterations = 1000
initiations = 10
eps = 0.001
lambda = 1.0