1. Is it necessary to set initial weights in prceptron convergence theorem to zero?
a) yes
b) no
Explanation: Initial setting of weights doesn’t affect perceptron convergence theorem.
2. The perceptron convergence theorem is applicable for what kind of data?
a) binary
b) bipolar
c) both binary and bipolar
d) none of the mentioned
Explanation: The perceptron convergence theorem is applicable for both binary and bipolar input, output data
3. Convergence in perceptron learning takes place if and only if:
a) a minimal error condition is satisfied
b) actual output is close to desired output
c) classes are linearly separable
d) all of the mentioned
Explanation: Linear separability of classes is the condition for convergence of weighs in perceprton learning
4. When line joining any two points in the set lies entirely in region enclosed by the set in M-dimensional space , then the set is known as?
a) convex set
b) concave set
c) may be concave or convex
d) none of the mentioned
Explanation: A convex set is a set of points in M-dimensional space such that line joining any two points in the set lies entirely in region enclosed by the set
5. Is it true that percentage of linearly separable functions will increase rapidly as dimension of input pattern space is increased?
a) yes
b) no
Explanation: There is decrease in number of linearly separable functions as dimension of input pattern space is increased.
6. If pattern classes are linearly separable then hypersurfaces reduces to straight lines?
a) yes
b) no
Explanation: Hypersurfaces reduces to straight lines, if pattern classes are linearly separable
7. I As dimensionality of input vector increases, what happens to linear separability?
a) increases
b) decreases
c) no effect
d) doesn’t depend on dimensionality
Explanation: Linear separability decreases as dimensionality increases.
8. In a three layer network, shape of dividing surface is determined by?
a) number of units in second layer
b) number of units in third layer
c) number of units in second and third layer
d) none of the mentioned
Explanation: Practically, number of units in second layer determines shape of dividing surface
9. In a three layer network, number of classes is determined by?
a) number of units in second layer
b) number of units in third layer
c) number of units in second and third layer
d) none of the mentioned
Explanation: Practically, number of units in third layer determines number of classes
10. If the output produces nonconvex regions, then how many layered neural is required at minimum?
a) 2
b) 3
c) 4
d) 5
Explanation:Adding one more layer of units to three layer can yield surfaces which can separate even nonconvex regions.