1,017 B
| title | date | mathjax | draft |
|---|---|---|---|
| Test Tex | 2022-08-26T22:28:10+08:00 | true | true |
Inline math: {{< texi \varphi >}}
Displayed math:
{{< texd \begin{aligned} \varphi &\Rightarrow \psi \\ \varnothing &\rightarrow A \end{aligned} >}}
$$
R_{\mu \nu} - {1 \over 2}g_{\mu \nu},R + g_{\mu \nu} \Lambda
= {8 \pi G \over c^4} T_{\mu \nu}
The equation $$(x_i \cdot x_j)^2$$ is called kernel function and is often written as $$k(x_i, x_j).
$$
\arg\max_\alpha \sum_j \alpha_j - \frac{1}{2} \sum_{j,k} \alpha_j, \alpha_k y_j y_k (x_j \cdot x_k)
$$
f(X) = \frac{1}{(2\pi)^{\frac{n}{2} |\Sigma|^{\frac{1}{2}}}} e^{ - \frac{1}{2} (X - \mu)^T \Sigma^{-1} (X - \mu)}
$$
\mu_i = \sum_{j=1}^N \frac{p_{ij} x}{n_i} \
\Sigma_i = \sum_{j=1}^N \frac{p_{ij} (x_j - \mu_i) (x_j - \mu_i)^T}{n_i}\
w_i = \frac{n_i}{N}
$$
S_i^{(t)} = \big { x_p : \big | x_p - \mu^{(t)}_i \big |^2 \le \big | x_p - \mu^{(t)}_j \big |^2 \ \forall j, 1 \le j \le k \big}
(The error above is a demo for incorrect formulas.)