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### Inhaltsverzeichnis

# Prüfung Pattern Recognition, SS 2015 (Oktober)

## Prüfer/Examiner

Elmar Noeth

## Prüfungsfragen und Antworten

**1. Bayesian Classifier**

- What is Bayesian Classifier and what is the bayesian decision rule ?

- Can you write down the Bayesian formula ?

- What is p(x/y) ?

- Why do we ignore the evidence p(x)?

- Then he drew two gausian distributions with unequal co-variance matrix and
asked me , what would be the shape of the decission boundry ?

- What happens to the decision boundary if the Prior P(y) of one class is
higher than the other ?

- What other distributions do we have other than Gausian ?

**2. Naive Bayes**

- How many parameters do we need to estimate for a 50 dimentional feature vector ?

- What should i do if I want to reduce the parameters to be estimated ?

- What does Naive bayes do ?

**3. Support Vector Machines**

- What do i do when i have linearly separable cases and I need a unique
decision boundary ?

- How does the support vector machine work ?

- Can you write the optimization rule in both hard and soft margin case with
constraints ?

- What will happen If i use perceptron in the same very case ?

- When does the perceptron converge ?

**4. Gausian Mixture models**

- What are gausian mixture models ?

- How do we solve the problem of gausian mixture models ?

- Explain how E-M Algorithm works ?

- What is the constraint of the P(k/y) ? Sum of P(k/y) = 1 ?

**5. Adaboost**
- What is a definition of a weak classifier ?

- What happens in Adaboost ?

- How are misclasified features treated in the next classifier ?

- How is the decission made in the end ?

- There is a modification to Adaboost algorithm , that is used for face
detection , what is it called and what does it do ?

- Why does subsequent classifier perform better ? (Because it has more features.)

## Stimmung/Atmosphere

Overall the atmosphere was very friendly , the professor wants to keeps
the discussion very interactive , He likes when you keep on discussing
more about the subject and the question asked. Mostly evaluations are done
when you use certain key words like a name of a classifier or something .

Note: 1.3