Raport de cercetare - Lorentz JÄNTSCHI

• Popularity
• Universal Approximators
• Nonlinearity
• Robustness
• Explanatory Knowledge
Software
R statistical environment
Supervised Learning
Handling Missing Data
Outliers
Non numerical variable remapping
Feature Selection
Classification Metrics
Receiver Operating Characteristic (ROC)
Regression Metrics
Validation method: how to estimate the performance?
MLP Training Algorithm
Local Minima with MLP
Overfitting
Case Study: Intensive Care Medicine (Classification)
Knowledge extraction (Decision Tree example for the renal organ)
Case Study: Lamb Meat Quality (Regression)
o Functional Networks
Introduction to functional networks
Differences between functional and artificial neural networks
Functional equations
Working with functional networks
Different models
Applications
o Multi-class SVMs, Theory
Yann Guermeur
Guaranteed risk for large margin multi-category classifiers
• Theoretical framework
• Basic uniform convergence result
• γ-ψ-dimensions
• Generalized Sauer-Shelah lemma
• Nature and rate of convergence
Multi-class SVM
• Multi-cathegory classification with binary SVMs
• Class of functions implemented by the M-SVMs
• Generalized formulation of the training algorithm
• Three main models of M-SVMs
• Some variants of the main models
• Margins and support vectors
Guaranteed risk for multi-class SVMs
• Bounds on the covering numbers
• Use of the Rademacher complexity
Model selection for multi-class SVMs
• Algorithms fitting the entire regularization path
• Bounds on the leave-one-aut cross-validation error
Open problems
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