Paper - Hogeschool Gent

Hoofdstuk 2. Theoretische achtergrond 13
Algoritme 2 Multinomiaal naive Bayes
1: function TrainMultinomiaal(C, D)
2: V ← ExtractheerW oordenschat(D)
3: N ← T elAantalDocumenten(D)
4: for each c ∈ C do
5: Nc ← T elAantalDocumentenInKlasse(D, c)
6: prior[c] ← Nc/N
7: tekstc ← ConcateneerAlleDocumentenInKlasse(D, c)
8: for each t ∈ V do
9: Tct ← T elAantalT okensV anT erm(textc, t)
10: for each t ∈ V do
11: voorw kans[t][c] ← Tct+1
t ′ Tct ′+1
12: return V, prior, voorw kans
13:
14: function DoeMNB(C, V, prior, voorw kans, d)
15: W ← ExtraheerW oordenschat(V, d)
16: for each c ∈ C do
17: score[c] ← log prior[c]
18: for each t ∈ W do
19: score[c]+ = log voorw kans[t][c]
20: return maxc∈C score[c]