I am thinking of an animal.
What kind of skin does it have?
Furry
Is it around the size of a bread box?
Yes
And so on\(\ldots\)
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decision tree DTG(I, T, E)
  if stop here(E)
    return point estimate(T, E)
  I' = pick(I)
  root = node(I')
  for v in dom(I')
    root.add child(
      DTG(I, T, {e ∈ E | val(e, I') = v})
  return root
if stop here(E) return point estimate(T, E)
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I' = pick(I)
target feature predict(D, I)
  if leaf(D)
    return D.label
  else
    return 
      predict(D.child[val(I, D.label)], I)
f w(\(I\)1, …, \(I\)n) = w0 + w1\(I\)1 + \(\cdots\) + wn\(I\)n = w0 + \(\sum\)j wj\(I\)j
w = (w0, …, wn)
pval w(e, T) = w0 + w1val(e, \(I\)1) + \(\cdots\) + wnval(e, \(I\)n) 
ErrorE(w) = \(\sum\)e \(\in\) E(val(e, T) - pval w(e, T))2

weight vector GD(I, T, E, η)
  wvec = random initialization
  repeat
    for e in E
      δ = val(e, T) - pval(wvec, e, T)
      for i in wvec.size
        w[i] = w[i] + ηδval(e, I[i])
  until done
  return wvec

f w(\(I\)1, …, \(I\)n) = w0 + w1\(I\)1 + \(\cdots\) + wn\(I\)n
f w(\(I\)1, …, \(I\)n) = f(w0 + w1\(I\)1 + \(\cdots\) + wn\(I\)n)
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f(x) = 1/(1 + e-x) 

Pr(h | e) = Pr(h \(\wedge\) e)/Pr(e)
Pr(e | h) = Pr(e \(\wedge\) h)/Pr(h)
Pr(h | e)Pr(e) = Pr(h \(\wedge\) e)
Pr(e | h)Pr(h) = Pr(e \(\wedge\) h)
Pr(e \(\wedge\) h) = Pr(h \(\wedge\) e)
Pr(h | e)Pr(e) = Pr(e | h)Pr(h)
Pr(flu | sniffles)Pr(sniffles) = Pr(sniffles | flu)Pr(flu) 
Pr(flu | sniffles)= Pr(sniffles | flu)Pr(flu)/Pr(sniffles) 

Pr(Category) and Pr(\(I\)j | Category)
then predict target-feature values.
Pr(Category | \(I\)1, …, \(I\)k) = \(\prod\) Pr(\(I\)i | Category)Pr(Category) 
Pr(Category = ti) = ni/\(\sum\)j nj
Pr(Category = ti | \(I\) = ij) = nij/\(\sum\)k nkj
ni is the ti count.
njk is the count of ij under tk.
| This page last modified on 2011 November 13. |