Traditional set theory set membership is fixed or exact either the member is in the set or not. There is only two crisp values true or false. In case of fuzzy logic there are many values. With weight say x the member is in the set.
c) Conditionally independent
Explanation: The semantics to derive a method for constructing Bayesian networks were led to the consequence that a node can be conditionally independent of its predecessors.
3. ____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic.
a) Fuzzy Relational DB
c) Fuzzy Set
d) None of the mentioned
c) Fuzzy Set
Explanation: Local structure is usually associated with linear rather than exponential growth in complexity.
b) Fully connected
Explanation: Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational database, FRDB, appeared in Maria Zemankova's dissertation.
d) All of the mentioned
Explanation: Entropy is amount of uncertainty involved in data. Represented by H(data).
a) Complete description of the domain
Explanation: A Bayesian network provides a complete description of the domain
b) IF-THEN rules
Explanation: Fuzzy set theory defines fuzzy operators on fuzzy sets. The problem in applying this is that the appropriate fuzzy operator may not be known. For this reason, fuzzy logic usually uses IF-THEN rules, or constructs that are equivalent, such as fuzzy associative matrices.
Rules are usually expressed in the form:
IF variable IS property THEN action
d) Answering probabilistic query
Explanation: Bayes rule can be used to answer the probabilistic queries conditioned on one piece of evidence.