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optimization_set [2016/05/20 22:04]
nikolaj
optimization_set [2016/10/16 16:31]
nikolaj
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 | @#55CCEE: context ​    | @#55CCEE: $ \langle Y, \le \rangle $ ... Non-strict partially ordered set | | @#55CCEE: context ​    | @#55CCEE: $ \langle Y, \le \rangle $ ... Non-strict partially ordered set |
 | @#55CCEE: context ​    | @#55CCEE: $ r:B\to Y $ | | @#55CCEE: context ​    | @#55CCEE: $ r:B\to Y $ |
-| @#FF9944: definition ​ | @#FF9944: $ O_r := \{\beta\in B\mid \forall(b\in ​X).\,​r(\beta)\le{r(b)}\}$ |+| @#FF9944: definition ​ | @#FF9944: $ O_r := \{\beta\in B\mid \forall(b\in ​B).\,​r(\beta)\le{r(b)}\}$ |
  
 ----- -----
-If ${\mathrm{min}(r)}\subseteq B$ denote the minimum values of $r$then +>todo 
 +>#tag  
 +>If p are parameters and c_p(xcurves with x_min(c_p)=f(p) knowntry to find x_min(c'​) by fitting c_p to c'. Now what's p here. Is there a scheme so that we can extend the list p to have guaranteed that there are parameters so that eventually c_p=c'?​
  
-$O_r = R^{-1}({\mathrm{inf}(r)})$+If ${\mathrm{min}(r)}\subseteq Y$ denote the minimum values of $r$, then  
 + 
 +$O_r = r^{-1}({\mathrm{min}(r)})$
  
 with $r^{-1}:​{\mathcal P}Y\to{\mathcal P}B$. with $r^{-1}:​{\mathcal P}Y\to{\mathcal P}B$.
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 (the indexed subspace of $X\to Y$ is called hypotheses space) (the indexed subspace of $X\to Y$ is called hypotheses space)
  
-and find from this set find the optimal fit (given by optimal $\beta\in B$) w.r.t. loss function $V:Y\to Y$ by optimizing+and find from this set find the optimal fit (given by optimal $\beta\in B$) w.r.t. loss function ​ 
 + 
 +$V:Y\times ​Y\to Y$  
 + 
 +by optimizing
  
 $r(\beta):​=V(f(\beta,​x),​y)$ $r(\beta):​=V(f(\beta,​x),​y)$
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 with loss function with loss function
  
-$V(y',y)=(y'-y)^2$+$V({\hat y},y)=({\hat y}-y)\cdot({\hat y}-y)$
  
-In practice, $x_i$ may be vectors and then $w$ is taken to be an inner product.+In practice, $x_i$ may be vectors and then $V$ is taken to be an inner product.
  
 === Reference === === Reference ===
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