Question 1

____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic.

Accepted Answer

A) fuzzy relational db 
B) ecorithms 
C) fuzzy set 
D) none of the mentioned 
A) fuzzy relational db 
B) ecorithms 
C) fuzzy set 
D) none of the mentioned

Question 2

How the compactness of the Bayesian network can be described?

Accepted Answer

A) locally structured 
B) fully structured 
C) partial structure 
D) all of the mentioned 
A) locally structured 
B) fully structured 
C) partial structure 
D) all of the mentioned

Question 3

How the entries in the full joint probability distribution can be calculated?

Accepted Answer

A) using variables 
B) using information 
C) both a & b 
D) none of the mentioned 
A) using variables 
B) using information 
C) both a & b 
D) none of the mentioned

Question 4

Which of the following is used for probability theory sentences?

Accepted Answer

A) conditional logic 
B) logic 
C) extension of propositional logic 
D) none of the mentioned 
A) conditional logic 
B) logic 
C) extension of propositional logic 
D) none of the mentioned

____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic.

How the compactness of the Bayesian network can be described?

How the entries in the full joint probability distribution can be calculated?

Which of the following is used for probability theory sentences?

AI and Knowledge Base Programming

Decision Trees, Machine Learning, and Heuristic Search in Artificial Intelligence

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Exam 1: Fuzzy Logic, Set Theory, and Bayesian Networks

____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic.

How the compactness of the Bayesian network can be described?

How the entries in the full joint probability distribution can be calculated?

Which of the following is used for probability theory sentences?

AI and Knowledge Base Programming

Decision Trees, Machine Learning, and Heuristic Search in Artificial Intelligence

Filters