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