The document discusses Daphne Koller's work on Naive Bayes models and classifiers. It introduces naive Bayes models, showing how they represent classes and conditionally independent features. It then explains how a naive Bayes classifier calculates probabilities of features given labels to classify new documents into labels or classes.

1. Daphne Koller
Bayesian Networks
Naïve Bayes
Probabilistic
Graphical
Models
Representation

2. Daphne Koller
Naïve Bayes Model
Class
X1 X2 Xn. . .
(Xi  Xj | C) for all Xi, Xj

3. Daphne Koller
Naïve Bayes Classifier
Class
X1 X2 Xn. . .

4. Daphne Koller
Naïve Bayes Classifier for Text I
cat dog buy. . .
Document Label
P(“buy” appears | Label)

5. Daphne Koller
Naïve Bayes Classifier for Text II
W1 W2 Wn. . .
Document Label Financial
cat dog buy sell …
Pets
0.001 0.001 0.02 0.02 …
0.02 0.03 0.003 0.001 …