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4 results for “supervised”

Formulae (4)

\hat{y} = \mathbf{w}^\top \mathbf{x} + b

Predicts a continuous value as a weighted sum of input features plus a bias.

\hat{y} = \dfrac{1}{1+e^{-(\mathbf{w}^\top \mathbf{x}+b)}}

Applies the sigmoid to a linear model to output a class probability.

J = \sum_{k=1}^{K}\sum_{\mathbf{x}\in C_k}\lVert \mathbf{x}-\mu_k\rVert^2

Clustering minimizes the total squared distance of points to their cluster centre.

\mathbf{C} = \tfrac{1}{n}\mathbf{X}^\top\mathbf{X},\;\; \mathbf{C}\mathbf{v}=\lambda\mathbf{v}

Finds directions of maximum variance via eigenvectors of the covariance matrix.