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这是Coursera上《机器学习技法》的课程笔记。
Aggregation models: mix or combine hypotheses for better performance, and it‘s a rich family.
Suppose we have $T$ hypotheses ,denoted by $g_1$, $g_2$, ... ,$g_T$. There are four different approachs to get a appregation model:
1.Select the best one $g_{t_*}$ from validation error $$G(x)=g_{t_*}(x) with t_*=argmin_{t \in \{1,2,...,T\}}E_{val}(g^-_t)$$
2.Mix all hypotheses uniformly $$G(x)=sign(\sum_{t=1}^T1*g_t(x))$$
3.mix all hypotheses non-uniformly $$G(x)=sign(\sum_{t=1}^T\alpha_t*g_t(x)) with \alpha_t \gep 0$$
NOTE: conclude select and mix uniformly.
4.Combine all hypotheses conditionally $$G(x)=sign(\sum_{t=1}^Tq_t(x)*g_t(x)) with q_t(x)\gep 0$$
NOTE: conclude non-uniformly
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原文地址：http://www.cnblogs.com/chym1009/p/5050953.html
