1. The instar learning law can be represented by equation?
a) ∆wjk= µ(bj – wjk), where the kth unit is the only active in the input layer
b) ∆wij= µ(bi – si) aj
c) ∆wij= µ(bi – si) aj Á(xi),wher Á(xi) is derivative of xi
d) ∆wij= µ(si) aj
Explanation: Follows from basic definition of outstar learning law
2. Is outstar a case of supervised learning?
a) yes
b) no
Explanation: Since weight adjustment depend on target output, it is supervised learning
3. Which of the following learning laws belongs to same category of learning?
a) hebbian, perceptron
b) perceptron, delta
c) hebbian, widrow-hoff
d) instar, outstar
Explanation: They both belongs to supervised type learning.
4. In hebbian learning intial weights are set?
a) random
b) near to zero
c) near to target value
d) near to target value
Explanation: Hebb law lead to sum of correlations between input & output, inorder to achieve this, the starting initial weight values must be small.
5. Weight state i.e set of weight values are determined by what kind of dynamics?
a) synaptic dynamics
b) neural level dynamics
c) can be either synaptic or neural dynamics
d) none of the mentioned
Explanation: Weights are best determined by synaptic dynamics, as it is one fastest & precise dynamics occurring.
6. Which is faster neural level dynamics or synaptic dynamics?
a) neural level
b) synaptic
c) both equal
d) insufficient information
Explanation:Since neural level dyna,ics depends on input fluctuations & these take place at every milliseconds
7. During activation dynamics does weight changes?
a) yes
b) no
Explanation: During activation dynamics, synaptic weights don’t change significantly & hence assumed to be constant
8. Activation dynamics is referred as?
a) short term memory
b) long term memory
c) either short or long term
d) both short & long term
Explanation: It depends on input pattern, & input changes from moment to moment, hence Short term memory.
9. Synaptic dynamics is referred as?
a) short term memory
b) long term memory
c) either short or long term
d) both short & long term
Explanation: Synaptic dynamics don’t change for a given set of training inputs, hence long term memory.
10. What is classification?
a) deciding what features to use in a pattern recognition problem
b) deciding what class an input pattern belongs to
c) deciding what type of neural network to use
d) none of the mentioned
Explanation: Follows from basic definition of classification.