Neural Networks Questions and Answers for experienced on “Learning Laws – 2”.
1. Reinforcement learning is also known as learning with critic?
A. yes
B. no
Answer: A
Clarification: Since this is evaluative & not instructive.
2. How many types of reinforcement learning exist?
A. 2
B. 3
C. 4
D. 5
Answer: B
Clarification: Fixed credit assignment, probablistic credit assignment, temporal credit assignment.
3. What is fixed credit assignment?
A. reinforcement signal given to input-output pair don’t change with time
B. input-output pair determine probability of postive reinforcement
C. input pattern depends on past history
D. none of the mentioned
Answer: A
Clarification: In fixed credit assignment, reinforcement signal given to input-output pair don’t change with time.
4. What is probablistic credit assignment?
A. reinforcement signal given to input-output pair don’t change with time
B. input-output pair determine probability of postive reinforcement
C. input pattern depends on past history
D. none of the mentioned
Answer: B
Clarification: In probablistic credit assignment, input-output pair determine probability of postive reinforcement.
5. What is temporal credit assignment?
A. reinforcement signal given to input-output pair don’t change with time
B. input-output pair determine probability of postive reinforcement
C. input pattern depends on past history
D. none of the mentioned
Answer: C
Clarification: In temporal credit assignment, input pattern depends on past history.
6. Boltzman learning uses what kind of learning?
A. deterministic
B. stochastic
C. either deterministic or stochastic
D. none of the mentioned
Answer: B
Clarification: Boltzman learning uses deterministic learning.
7. Whats true for sparse encoding learning?
A. logical And & Or operations are used for input output relations
B. weight corresponds to minimum & maximum of units are connected
C. weights are expressed as linear combination of orthogonal basis vectors
D. change in weight uses a weighted sum of changes in past input values
Answer: A
Clarification: sparse encoding learning employs Logical And & Or operations are used for input output relations.
8. Whats true for Drive reinforcement learning?
A. logical And & Or operations are used for input output relations
B. weight corresponds to minimum & maximum of units are connected
C. weights are expressed as linear combination of orthogonal basis vectors
D. change in weight uses a weighted sum of changes in past input values
Answer: D
Clarification: In Drive reinforcement learning, change in weight uses a weighted sum of changes in past input values.
9. Whats true for Min-max learning?
A. logical And & Or operations are used for input output relations
B. weight corresponds to minimum & maximum of units are connected
C. weights are expressed as linear combination of orthogonal basis vectors
D. change in weight uses a weighted sum of changes in past input values
Answer: B
Clarification: Min-max learning involves weights which corresponds to minimum & maximum of units connected.
10. Whats true for principal component learning?
A. logical And & Or operations are used for input output relations
B. weight corresponds to minimum & maximum of units are connected
C. weights are expressed as linear combination of orthogonal basis vectors
D. change in weight uses a weighted sum of changes in past input values
Answer: C
Clarification: principal component learning involves weights that are expressed as linear combination of orthogonal basis vectors.
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