CSI 5810 - Information Retrieval and Knowledge Discovery

Assignment Help Management Information Sys

Reference no: EM132395932

CSI 5810 - Information Retrieval and Knowledge Discovery, Oakland University USA

Assignment

1. The following examples from a two-class classification problem are given:

Class1: [2 2]^T, [3 5]^T; Class 2 [1 3]^T, [-1 -0.5]^T
Starting with an augmented weight vector, [1 1 1]^T, determine a solution vector for above data using the perceptron learning rule. Show first five steps of weight vector updating.

2. Suppose you are given a collection of weak learners where each learner is able to operate with 55% accuracy. You combine seven of these learners with a majority rule for final output. What will be the accuracy of the ensemble? How many learners will be needed if the accuracy desired is at least 90%.

3. Consider the following eight records; each record is described by two quantitative attributes:

A = (2, 10)^t, B = (2, 5)^t, C = (8, 4)^t, D = (5, 8)^t, E = (7, 5)^t, F = (6, 4)t G = (1, 2)^t, H = (4, 9)^t.

Let records “A”, “B”, “G”, and “H” be from class 1 and the remaining four records from class 2. Using this information, construct the Fisher’s linear discriminant function for this problem and determine the class label for the point M = (3, 3)^t.

4. Consider the following six examples with three attributes:

Example #	Color	Shape	Size	Class
1	Red	Square	Big	+
2	Blue	Square	Big	+
3	Red	Round	Small	-
4	Green	Square	Small	-
5	Red	Round	Big	+
6	Green	Square	Big	-

Determine the best attribute for root node of a decision tree classifier for above data. Use Gini index for attribute selection.

5. Consider the network of neurons shown on the next page. With the current input and the weights as shown, determine the output of the network. Assume sigmoidal activation function. With the specified target output of “1”, determine the value of the updated weight for the connection linking U₃ and U₅, and U₃ and U₁.

Reference no: EM132395932

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