1. Learning is a?
a) slow process
b) fast process
c) can be slow or fast in general
d) can’t say
Explanation: Learning is a slow process
2. What are the requirements of learning laws?
a) convergence of weights
b) learning time should be as small as possible
c) learning should use only local weights
d) all of the mentioned
Explanation:These all are the some of basic requirements of learning laws
3. Memory decay affects what kind of memory?
a) short tem memory in general
b) older memory in general
c) can be short term or older
d) none of the mentioned
Explanation: Memory decay affects short term memory rather than older memories
4. What are the requirements of learning laws?
a) learning should be able to capture more & more patterns
b) learning should be able to grasp complex nonliear mappings
c) convergence of weights
d) all of the mentioned
Explanation: These all are the some of basic requirements of learning laws.
5. How is pattern information distributed?
a) it is distributed all across the weights
b) it is distributed in localised weights
c) it is distributed in certain proctive weights only
d) none of the mentioned
Explanation: pattern information is highly distributed all across the weights
6. What is supervised learning?
a) weight adjustment based on deviation of desired output from actual output
b) weight adjustment based on desired output only
c) weight adjustment based on actual output only
d) none of the mentioned
Explanation: Supervised learning is based on weight adjustment based on deviation of desired output from actual output.
7. Supervised learning may be used for?
a) temporal learning
b) structural learning
c) both temporal & structural learning
d) none of the mentioned
Explanation: Supervised learning may be used for both temporal & structural learning
8. What is structural learning?
a) concerned with capturing input-output relationship in patterns
b) concerned with capturing weight relationships
c) both weight & input-output relationships
d) none of the mentioned
Explanation: Structural learning deals with learning the overall structure of network in a macroscopic view.
9. What is temporal learning?
a) concerned with capturing input-output relationship in patterns
b) concerned with capturing weight relationships
c) both weight & input-output relationships
d) none of the mentioned
Explanation: Temporal learning is concerned with capturing weight relationships
10. What is unsupervised learning?
a) weight adjustment based on deviation of desired output from actual output
b) weight adjustment based on desired output only
c) weight adjustment based on local information available to weights
d) none of the mentioned
Explanation: Unsupervised learning is purely based on adjustment based on local information available to weights