4 Machine learning - UPMocw.upm.es/.../mod_label/intro/4_machine_learning-ppt.pdfRedes...
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CVG-UPM COMPUTER VISION Machine Learning & Neural Networks 4.- Machine Learning General Methodology by Pascual Campoy Grupo de Visión por Computador U.P.M. - DISAM 2 CVG-UPM COMPUTER VISION Machine Learning and Neural Networks P. Campoy P. Campoy P. Campoy Machine Learning General Methodology Objectives Supervised and not supervised learning Learning challenges Building machine learning models Errors and validation
Transcript of 4 Machine learning - UPMocw.upm.es/.../mod_label/intro/4_machine_learning-ppt.pdfRedes...
CVG-UPMC
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Machine Learning & Neural Networks
4.- Machine Learning
General Methodology
byPascual Campoy
Grupo de Visión por ComputadorU.P.M. - DISAM
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CVG-UPM
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Machine LearningGeneral Methodology
Objectives
Supervised and not supervised learning
Learning challenges
Building machine learning models
Errors and validation
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CVG-UPMC
OM
PU
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Learning objectives
?
y
x2
?
x1
x2
Find a model that predicts the right outputfor a new input, given previous outputs
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CVG-UPM
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Supervised learning
area
leng
th
Feature space
?
Supervised learining concept Working structure
y1..ym
.
.xn
x1
yd1
ydm
+ -..
Rn ⇒ Rm function generalitation
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CVG-UPMC
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Unsupervised learning
Feature space
Unsuprvised learnng concept
?
area
leng
th
Working structure
y1..ym
.
.xn
x1
Clustering
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CVG-UPM
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Leaning challenges
lack of learning instances
noise
?x1
x2
x1
x2
??
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CVG-UPMC
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Leaning challenges
Aprendizaje basado en la f.d.d. de lasmuestras ⇒ posible incorrelación entre lacantidad de muestras y su importancia en lasalida
p(x) h(x)
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CVG-UPM
CO
MP
UT
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VIS
ION
Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Machine LearningGeneral Methodology
Objectives
Supervised and not supervised learning
Learning challenges
Building machine learning models
Errors and validation
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CVG-UPMC
OM
PU
TE
R V
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Building machine learning models:levels
Determinación de laestructura interna
(manual/automático)
Elección del modelo(manual)
modeloAjuste de parámetros(automático)
mue
stra
s e
ntre
nam
ient
o
Error deentrenamiento
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CVG-UPM
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Parameter calculation
Optimización de un índice
Función de coste :Lj(x) = Σ L(wj/wi) P(wi/x)
Error cuadrático medioE = ∑ ∑ ( yk
n - ydkn )2
n k
Compromiso:grado de optimización frente a tiempo de cálculo
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CVG-UPMC
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Building machine learning models:model structure
Determinación de laestructura interna
(manual/automático)
Elección del modelo(manual)
modeloAjuste de parámetros(automático)
mue
stra
s de
te
st
error de test
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CVG-UPM
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Model structure evaluation
Universo deentradas Conjunto de test
o de validación
Validación cruzada:realizar modelos entrenados con V muestras y validadoscon conjuntos de test de N-V muestras
!
N
V
"
# $ %
& '
Conjunto deentrenamiento
modelo
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CVG-UPMC
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Building machine learning models:model types
Determinación de laestructura interna
(manual/automático)
Elección del modelo(manual)
MODELOAjuste de parámetros(automático)
error de validación
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CVG-UPM
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Model selection
Matemáticos:- polinomios, splines, B-splines, ...
Estadísticos:- ARX, ARMAX, Markov, Box-Jenkins, ...
Redes Neuronales:- MLP, RBF, SOM, ART, ...
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CVG-UPMC
OM
PU
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Machine LearningGeneral Methodology
Objectives
Supervised and not supervised learning
Learning challenges
Building machine learning models
Errors and validation
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CVG-UPM
CO
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UT
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VIS
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Numero de muestras de la red neuronal
error
Error de test
Error bayesiano
Error de entrenamiento
Error de entrenamiento yerror de test o de validación
Influencia nº muestras
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Influencia nº g.d.l. del modelo
g.d.l. del modelo
error de test
x1
x2
x1
x2
Infra-aprendizaje
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CVG-UPM
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Influencia nº g.d.l. del modelo
x1
x2
x1
x2
g.d.l. del modelo
error de test
Sobre-aprendizaje
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CVG-UPMC
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
Influencia nº g.d.l. del modelo
x1
x2
x2
g.d.l. del modelo
error de test
Aprendizajex1
CVG-UPM
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
CVG-UPMC
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Machine Learning and Neural NetworksP. CampoyP. CampoyP. Campoy
