(5)(6)v i e w e r m o v i e top bottom rating LEARNING (7

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v i e w e r m o v i e

top

bottom

rating

LEARNING

· · · · · ·

• x

• y

• f : X → Y

• ( x ₁ , y ₁ ) , ( x ₂ , y ₂ ) , · · · , ( x _N , y _N )

↓ ↓ ↓

• g : X → Y

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

y

( , ), ... , ( , )

H

A

g ~ f ~

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

x₁ y₁ x_N y_N ( , ), ... , ( , )

H

A

g ~ f ~

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H = { h } g ∈ H

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x = ( x ₁ , · · · , x _d )

d

!

i=1

w _i x _i > ,

d

!

i =1

w _i x _i < . h ∈ H

h ( x ) =

"" _d

#

i =1

w _i x _i

$

−

$

+ +

+ _ +

_ _

_

+

+ +

+ + _

_

_

_ +

h ( x ) =

"" _d

#

i =1

w _i x _i

$

+ w ₀

$

x ₀ = 1 h ( x ) =

" _d

#

i =0

w _i x _i

$

h ( x ) = ( w x )

h ( x ) = ( w x )

( x ₁ , y ₁ ) , ( x ₂ , y ₂ ) , · · · , ( x _N , y _N )

( w x _n ) %= y _n

w ← w + y _n x _n

w+ x y

y w+ x y= +1

w x

x w

−1

y=

+ +

+ +

+ _

_ _

_

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w ← w + y x ( x , y )

• t = 1 , 2 , 3 , · · ·

( x ₁ , y ₁ ), ( x ₂ , y ₂ ), · · · , ( x _N , y _N )

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25

5 1

Mass

Size

10

25

5 1

Mass

Size

10

Mass

Size

f = − 1

f = +1

f = ?