theoretic专题

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selecti2

第三节主要以理论推导为主,主要是为了推导出最大条件似然问题可以近似为最小化条件互信的问题: arg ⁡ max ⁡ θ L ( θ , D ) = arg ⁡ min ⁡ θ I ( X θ ~ ; Y ∣ X θ ) \arg\max_{\theta}\mathcal{L}(\theta,\mathcal{D})=\arg\min_\theta I(X_{\tilde\theta};Y|X_\

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selecti

目录 1、文章信息2、主要思想2.1信息熵:2.2 基于互信息的滤波算法 1、文章信息 Title: Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection Author: Gavin Brown, Adam Pocock, Mi