japanese_economy_car.txt

Initial setup:
G = { < ?, ?, ?, ?, ? > }
S = { < {}, {}, {}, {}, {} > }

Iteration 1: < < Japan, Honda, Blue, 1980, Economy >, 1 >

G = { < ?, ?, ?, ?, ? > }
S = { < Japan, Honda, Blue, 1980, Economy > }

Iteration 2: < < Japan, Toyota, Green, 1970, Sports >, 0 >

G = { < ?, Honda, ?, ?, ? >,
< ?, ?, Blue, ?, ? >,
< ?, ?, ?, 1980, ? >,
< ?, ?, ?, ?, Economy > }
S = { < Japan, Honda, Blue, 1980, Economy > }

Iteration 3: < < Japan, Toyota, Blue, 1990, Economy >, 1 >

G = { < ?, ?, Blue, ?, ? >,
< ?, ?, ?, ?, Economy > }
S = { < Japan, ?, Blue, ?, Economy > }

Iteration 4: < < USA, Chrysler, Red, 1980, Economy >, 0 >

G = { < ?, ?, Blue, ?, ? >,
< Japan, ?, ?, ?, Economy > }
S = { < Japan, ?, Blue, ?, Economy > }

Iteration 5: < < Japan, Honda, White, 1980, Economy >, 1 >

G = { < Japan, ?, ?, ?, Economy > }
S = { < Japan, ?, ?, ?, Economy > }

Candidate elimination algorithm learnt hypothesis: < Japan, ?, ?, ?, Economy >
after 5 training examples