draft lecture01 april 03
TRANSCRIPT
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v i e w e r
m o v i e
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rating
LEARNING
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x
y
f : X Y
(x
1, y
1), (x
2, y
2), , (xN, yN)
g : X Y
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f:
(ideal credit approval function)
(historical records of credit customers)
HYPOTHESIS SET
(set of candidate formulas)
ALGORITHM
LEARNING FINAL
HYPOTHESIS
UNKNOWN TARGET FUNCTION
(final credit approval formula)
TRAINING EXAMPLES
X Y
x y x yNN11
( , ), ... , ( , )
H
A
g ~ f~
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f:
(ideal credit approval function)
(historical records of credit customers)
HYPOTHESIS SET
(set of candidate formulas)
ALGORITHM
LEARNING
UNKNOWN TARGET FUNCTION
(final
TRAINING EXAMPLES
X Y
x y x yNN11
( , ), ... , ( , )
H
A
H = {h} g H
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x = (x1, , xd)
di=1
wixi > ,
di=1
wixi < .
h H
h(x) =
d
i=1
wixi
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+
++
+_
_
_
_
+
h(x) =
d
i=1
wi xi
+ w0
x0 = 1
h(x) =
d
i=0
wi xi
h(x) = (w x)
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h(x) = (w x)
(x1, y1), (x2, y2), , (xN, yN)
(w
xn) = yn
w w+ ynxn
yw+ x
y=+1
w
1y=
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_
_
w w+ yx
(x, y)
t = 1, 2, 3,
(x1, y1), (x2, y2), , (xN, yN)
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=
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25
5
1
Mass
Size
10
25
5
1
Mass
S
10
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Mass
Size
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f =
1
f = +1
f = ?