Repaso Anal Discriminante - Tomando logaritmos obtenemos la...

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Análisis Discriminante
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Dos problemas principales de clasificación Discriminación (clasificación supervisada) Agrupamiento (Cluster Analysis) (clasificación no supervisada)
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El problema estadístico de discriminación Dadas dos poblaciones de elementos con distribución conocida clasificar un nuevo elemento en una de las dos poblaciones
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Ejemplos Clasificar: Restos de un cráneo como homínido o no Un solicitante de un crédito como solvente o no Un paciente con cáncer o no Una obra de arte al autor A o B. Diseño de máquinas de clasificación (para cartas, billetes,monedas, etc.)
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Datos habituales Matriz de datos Elemento n1 Grupo A Grupo B Elemento n2 Elemento 1 Elemento 1
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Análisis de genes
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Identificación de rasgos .23 …. Matriz Identificar Rostro(pauta) Clasificar como conocido o no
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Problemas de clasificación A 4 ? 100 euros? 1000 dracmas?
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Planteamiento general
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Costes
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Caso particular: Poblaciones normales
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Unformatted text preview: Tomando logaritmos, obtenemos la clasificación en P2 Interpretación de la regla: Simplificando lo que es común en ambos miembros quedan solo términos lineales en x Probab. de error y a posteriori Interpretación Clasificar en A Clasificar en B A B Fisher A B Clasificar en población B Clasificar en A Enfoque de Fisher Varios grupos ejemplo Discriminación cuadrática Clasificación logística Problemas del modelo lineal • No hay garantía de que las probabilidades estén entre cero y uno, pueden tomar valores negativos o mayores que uno. • Es heterocedástico. Si estimamos el modelo lineal con variable de clasificación –1 +1 se obtiene la función lineal discriminante. Otros enfoques: • Redes neuronales • Métodos no paramétricos • Máquinas de vector soporte redes neuronales Aproximar la función mediante Máquinas de vector soporte...
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