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Learning Vector Quantization LVQ

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Machine Learning & Neural Networks Eric Postma IKAT
Contenido ,[object Object],[object Object],[object Object],[object Object]
Perceptrón
Deducción de la regla delta de aprendizaje Target output Actual output h = i
Perceptrón Multicapa
Función sigmoidal ,[object Object],[object Object]
Deducción de la regla delta generalizada
Función de error (LMS)
Ajuste del vector de pesos (capa oculta – salida)
Ajuste del vector de pesos (capa de entrada – capa oculta)
Propagación hacia adelante y retropropagación
El problema de muestras dispersas ,[object Object],outlier
Solución al problema de los puntos dispersos ,[object Object],[object Object]
El problema de inversión x t x t Functional mapping Non-functional inverse mapping
Ejemplo: análisis espectral
Alternativa: modelos mixtos ,[object Object],[object Object],[object Object]
Solución m.b.v. Modelo mixto x t Non-functional inverse mapping
Weight decay ,[object Object],[object Object],[object Object]
Regla delta generalizada con Weight Decay ,[object Object]
Regla de aprendizaje con weight decay
Cushings Dataset
Algunos resultados… ,[object Object],[object Object],[object Object],[object Object],[object Object]
2 hidden (perfect fit)
2 hidden (perfect fit)
2 hidden, lambda = 0.001 (smoother) = Lokaal minimum
2 hidden, lambda = 0.01 = Lokaal minimum
5 hidden, lambda = 0.01
20 hidden, lambda = 0.01
Learning Vector Quantisation (LVQ)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Self-organizing Feature Maps Teuvo Kohonen
Inspiración biológica ,[object Object],[object Object],[object Object],[object Object],[object Object]
La diferencia más importante con LVQ ,[object Object]
 
 
 
 
 
 
 
SOM toegepast op tijdreeksen
Matlab SOM toolbox
Los datos (3D)
Unified Distance Matrix
Sammon mapping
Hit histogram 1 (numeric)
Hit histogram 2 (size)

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