List of Availble Algorithms

Notation

To avoid confusion of terms and notations, we make the following basic definitions:

• X: feature matrix

X = \begin{pmatrix}
 x_{11} & x_{12} & \dots & x_{1m} \\
 x_{21} & x_{22} & \dots &  x_{2m} \\
 \vdots & \vdots & \dots &  \vdots \\
 v_{n1} & x_{n2} & \dots &  x_{nm} \\
 \end{pmatrix}

Here, each row denotes a sample (or an observation) x_i (i=1, \dots, n) or X_{i.} . Each column denotes a feature, X_{.j} (j = 1, \dots , p)

• Y: the label in supervised learning

Y = \begin{pmatrix}
y_1\\
y_2\\
\vdots \\
y_n \\
\end{pmatrix}

We have y_i \in \mathcal{R} for regression problem, and y_i \in \mathcal{Z} for classification

• Training/Validation dataset: in XFL, we mainly use two types of dataset, one for training and the other for validation. We use the superscript "train", "val" to identify them. For example, X^{train}, X^{val} denote training dataset, validation dataset respectively.

List of Algorithms