Gunel, Beliz

To classify something, find things that are similar and label it with the same class as the most similar thing.

The feature space is \(N-d\), where \(N\) is the number of features. Each instance is mapped to a point. The descriptive features become the axes.

The Similarity Metric

Mathematically, it must conform to these 4 criteria:

• Non-negative: \(f(a, b) \ge 0\)
• Identity: \( f(a, b) = 0 \iff a = b \)
• Symmetry: \( f(a, b) = f(b, a) \)
• Triangular inequality: \( f(a, b) \le f(a, c) + f(c, b) \)

To classify something, find things that are similar and label it with the same class as the most similar thing.

The feature space is \(N-d\), where \(N\) is the number of features. Each instance is mapped to a point. The descriptive features become the axes.

The Similarity Metric

Mathematically, it must conform to these 4 criteria:

• Non-negativity: \(f(a, b) \ge 0\)
• Identity of Indiscernables: \( f(a, b) = 0 \iff a = b \)
• Symmetry: \( f(a, b) = f(b, a) \)
• Subaddivity (Triangular inequality): \( f(a, b) \le f(a, c) + f(c, b) \)

WWW ‘21: The Web Conference 2021

REST: Relational Event-Driven Stock Trend Forecasting

REST, an event-driven stock trend forecasting framework, that overcomes two limitations of existing event-driven models. Models the stock context, and learns the effect of event information on the stocks under different contexts. Constructs a stock graph and designs a new propagation layer to propagate the effect of event information from related stocks. #stock-trend-forecasting #computational-finance

#graph-based-learning