University AI - Main Supervised Learning Tasks

Main Supervised Learning Tasks

A supervised MLP learns a transformation $\varphi :{\mathbb{R}}^d\to {\mathbb{R}}^m$ which in principle, may be applied to :

• Function approximation: $\underline{y}=\varphi (\underline{x})$.
• Regression (linear or non-linear) : $\underline{y}=\varphi (\underline{x})+\underline{\epsilon }$, where $\underline{\epsilon }$ is the multivariate Gaussian noise.
• Pattern classification: $\underline{y}=(g_1(\underline{x}),\dots ,g_c(\underline{x}))$.
  

2-Class Classification: Depending on how we descrive the model, we might obtain a linear or non-linear classifier. This is heavily influenced by the number of hidden layers in the MLP:

• If we have no hidden layers and a sigmoid activation function, the model will result into a linear classifier.
• While if we have at least 1 hidden layer (still considering sigmoid activation functions) the model will be non-linear

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