Pattern Classification MCQ

Test your knowledge with important Pattern Classification MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30 + more Pattern Classification MCQs at Bissoy

• If the output produces nonconvex regions, then how many layered neural is required at minimum?
  ক

  2

  খ

  3

  গ

  4

  ঘ

  5
• Intersection of convex regions in three layer network can only produce convex surfaces, is the statement true?
  ক

  yes

  খ

  no
• Intersection of linear hyperplanes in three layer network can only produce convex surfaces, is the statement true?
  ক

  yes

  খ

  no
• In a three layer network, number of classes is determined by?
  ক

  number of units in second layer

  খ

  number of units in third layer

  গ

  number of units in second and third layer

  ঘ

  none of the mentioned
• In a three layer network, shape of dividing surface is determined by?
  ক

  number of units in second layer

  খ

  number of units in third layer

  গ

  number of units in second and third layer

  ঘ

  none of the mentioned
• As dimensionality of input vector increases, what happens to linear separability?
  ক

  increases

  খ

  decreases

  গ

  no effect

  ঘ

  doesn’t depend on dimensionality
• If pattern classes are linearly separable then hypersurfaces reduces to straight lines?
  ক

  yes

  খ

  no
• Is it true that percentage of linearly separable functions will increase rapidly as dimension of input pattern space is increased?
  ক

  yes

  খ

  no
• 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?
  ক

  convex set

  খ

  concave set

  গ

  may be concave or convex

  ঘ

  none of the mentioned
• Convergence in perceptron learning takes place if and only if:
  ক

  a minimal error condition is satisfied

  খ

  actual output is close to desired output

  গ

  classes are linearly separable

  ঘ

  all of the mentioned
• If e(m) denotes error for correction of weight then what is formula for error in perceptron learning model: w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight
  ক

  e(m) = n(b(m) – s(m)) a(m)

  খ

  e(m) = n(b(m) – s(m))

  গ

  e(m) = (b(m) – s(m))

  ঘ

  none of the mentioned
• w(m + 1) = w(m) + n(b(m) – s(m)) a(m), where b(m) is desired output, s(m) is actual output, a(m) is input vector and ‘w’ denotes weight, can this model be used for perceptron learning?
  ক

  yes

  খ

  no
• The perceptron convergence theorem is applicable for what kind of data?
  ক

  binary

  খ

  bipolar

  গ

  both binary and bipolar

  ঘ

  none of the mentioned
• Is it necessary to set initial weights in prceptron convergence theorem to zero?
  ক

  yes

  খ

  no
• Two classes are said to be inseparable when?
  ক

  there may exist straight lines that doesn’t touch each other

  খ

  there may exist straight lines that can touch each other

  গ

  there is only one straight line that separates them

  ঘ

  all of the mentioned
• If two classes are linearly inseparable, can perceptron convergence theorem be applied?
  ক

  yes

  খ

  no
• When two classes can be separated by a separate line, they are known as?
  ক

  linearly separable

  খ

  linearly inseparable classes

  গ

  may be separable or inseparable, it depends on system

  ঘ

  none of the mentioned
• In perceptron learning, what happens when input vector is correctly classified?
  ক

  small adjustments in weight is done

  খ

  large adjustments in weight is done

  গ

  no adjustments in weight is done

  ঘ

  weight adjustments doesn’t depend on classification of input vector
• On what factor the number of outputs depends?
  ক

  distinct inputs

  খ

  distinct classes

  গ

  both on distinct classes & inputs

  ঘ

  none of the mentioned
• What is the objective of perceptron learning?
  ক

  class identification

  খ

  weight adjustment

  গ

  adjust weight along with class identification

  ঘ

  none of the mentioned