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Oct 10, 2020 | » | Similarity Measures
2 min; updated Sep 5, 2022
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: \( f(a, b) \le f(a, c) + f(c, b) \) Why are non-negativity and triangular inequality important?... |

Oct 10, 2020 | » | Similarity Measures
2 min; updated Sep 5, 2022
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): \( f(a, b) \le f(a, c) + f(c, b) \) Why are non-negativity and triangular inequality important?... |

Oct 4, 2021 | » | Online Markets
4 min; updated Sep 5, 2022
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 The value of stock trend forecasting is not unanimous, e.... |