1. what are affine transformations?
a) addition of bias term (-1) which results in arbitrary rotation, scaling, translation of input pattern
b) addition of bias term (+1) which results in arbitrary rotation, scaling, translation of input pattern
c) addition of bias term (-1) or (+1) which results in arbitrary rotation, scaling, translation of input pattern
d) none of the mentioned
Explanation: It follows from basic definition of affine transformation.
2. Can a artificial neural network capture association if input patterns is greater then dimensionality of input vectors?
a) yes
b) no
Explanation: By using nonlinear processing units in output layer.
3. By using only linear processing units in output layer, can a artificial neural network capture association if input patterns is greater then dimensionality of input vectors?
a) yes
b) no
Explanation: There is need of non linear processing units
4. Number of output cases depends on what factor?
a) number of inputs
b) number of distinct classes
c) total number of classes
d) none of the mentioned
Explanation: Number of output cases depends on number of distinct classes.
5. For noisy input vectors, Hebb methodology of learning can be employed?
a) yes
b) no
Explanation: For noisy input vectors, no specific type of learning method exist
6. What is the objective of perceptron learning?
a) class identification
b) weight adjustment
c) adjust weight along with class identification
d) none of the mentioned
Explanation:The objective of perceptron learning is to adjust weight along with class identification
7. On what factor the number of outputs depends?
a) distinct inputs
b) distinct classes
c) both on distinct classes & inputs
d) none of the mentioned
Explanation: Number of outputs depends on number of classes
8. In perceptron learning, what happens when input vector is correctly classified?
a) small adjustments in weight is done
b) large adjustments in weight is done
c) no adjustments in weight is done
d) weight adjustments doesn’t depend on classification of input vector
Explanation: No adjustments in weight is done, since input has been correctly classified which is the objective of the system.
9. When two classes can be separated by a separate line, they are known as?
a) linearly separable
b) linearly inseparable classes
c) may be separable or inseparable, it depends on system
d) none of the mentioned
Explanation: Linearly separable classes, functions can be separated by a line
10. Two classes are said to be inseparable when?
a) there may exist straight lines that doesn’t touch each other
b) there may exist straight lines that can touch each other
c) there is only one straight line that separates them
d) all of the mentioned
Explanation: Linearly separable classes, functions can be separated by a line