Identify - Pattern Recognition - Assignment, Exercises of Computer Science

These are the Assignment of Pattern Recognition which includes Squared Mahalanobis, Weighted Version, Squared Euclidean, Dimensional Binary Patterns, Euclidean Distance, Satisfy Symmetry etc.Key important points are: Identify, Decision Tree, Training Data, Axis-Parallel Decision, Funding, Research Output, Number of Students

Typology: Exercises

2012/2013

Uploaded on 03/28/2013

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Assignment
1. There are four coins 1, 2, 3, 4 out of which three coins are of equal
weight and one coin is heavier. Use a decision tree to identify the
heavier coin.
2. Consider the three-class problem characterized by the training data
given in the following table. Obtain the axis-parallel decision tree for
the data.
Professor Number of Students Funding Research Output
Sam 3 Low Medium
Sam 5 Low Medium
Sam 1 High High
Pam 1 High Low
Pam 5 Low Low
Pam 5 High Low
Ram 1 Low Low
Ram 3 High Medium
Ram 5 Low High
3. Consider a data set of 10 patterns which is split into 3 classes at a node
in a decision tree where 4 patterns are from class 1, 5 from class 2, and
1 from class 3. Computer Entropy Impurity. What is the Variance
Impurity?
4. For the data in problem 3, compute the Gini Impurity. How about the
Misclassification Impurity?
5. Consider the data given in the following table for a three-class problem.
If the set of 100 training patterns is split using a variable X into two
parts represented by the left and right subtrees below the decision node
as shown in the table, compute the entropy impurity.
6. Compute the variance impurity for the data given in problem 5. How
about the misclassification impurity?
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Assignment

  1. There are four coins 1, 2, 3, 4 out of which three coins are of equal weight and one coin is heavier. Use a decision tree to identify the heavier coin.
  2. Consider the three-class problem characterized by the training data given in the following table. Obtain the axis-parallel decision tree for the data. Professor Number of Students Funding Research Output Sam 3 Low Medium Sam 5 Low Medium Sam 1 High High Pam 1 High Low Pam 5 Low Low Pam 5 High Low Ram 1 Low Low Ram 3 High Medium Ram 5 Low High
  3. Consider a data set of 10 patterns which is split into 3 classes at a node in a decision tree where 4 patterns are from class 1, 5 from class 2, and 1 from class 3. Computer Entropy Impurity. What is the Variance Impurity?
  4. For the data in problem 3, compute the Gini Impurity. How about the Misclassification Impurity?
  5. Consider the data given in the following table for a three-class problem. If the set of 100 training patterns is split using a variable X into two parts represented by the left and right subtrees below the decision node as shown in the table, compute the entropy impurity.
  6. Compute the variance impurity for the data given in problem 5. How about the misclassification impurity?

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Class Label Total No in Class No. in Left Subtree No. in Right Subtree 1 40 40 0 2 30 10 20 3 30 10 20

  1. Consider the two-class problem in a two-dimensional space character- ized by the following training data. Obtain an axis-parallel decision tree for the data. Class 1 : (1, 1) t , (2, 2) t , (6, 7) t , (7, 7) t Class 2 : (6, 1) t , (6, 2) t , (7, 1) t , (7, 2) t
  2. Consider the data given in problem 7. Suggest an oblique decision tree.

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