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| CS607Current FinalTerm Paper 1 March 2017 |
timing 11:00 am
Paper was very Easy and almost 90% paper from Moaz and Arslan files
Write down the task for which connectionist approach is well suited. 5 marks
Answer:- (Page 181) Tasks for which connectionist approach is well suited include:
• Classification • Fruits – Apple or orange
• Pattern Recognition • Finger print, Face recognition
• Prediction • Stock market analysis, weather forecast
“Boolean logic is a subset of fuzzy logic.” Do you agree with the statement or not? Give reason to support your answer.(Marks 3)
Answer:- (Page 147) Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth -- truth values between "completely true" and "completely false". For example, There are two persons. Person A is standing on the left of person B. Person A is definitely shorter than person B. But if boolean gauge has only two readings, 1 and 0, then a person can be either all or short. Let‟s say if the cut off point is at 5 feet 10 inches then all the people having a height greater than this limit are taller and the rest are short.
Five parts of fuzzy inference process? 5 marks
Answer:- (Page 154) • Fuzzification of the input variables • Application of fuzzy operator in the antecedent (premises) • Implication from antecedent to consequent • Aggregation of consequents across the rules • Defuzzification of output
Write the code in CLIPS to add two digits 3 and 4? (2)
Answer:
CLIPS> (+ 3 4)
Which type of ordering in hypothesis spaces is best suited from h <?, ?> hypothesis and towards h <φ, φ> hypothesis (3 marks)
Answer:
All the hypothesis in h can be ordered according to their generality, starting from the <?, ?> which is the most general hypothesis since it always classifies all the instances as positive. On the contrary, we have < φ, φ >which is the most specific hypothesis since it doesn’t classify a single instance as positive.
Unsupervised Methodology (5 marks)
Answer:
Given a set of examples with no labeling, group them into sets called clusters.
A cluster represents some specific underlying patterns in data.
Useful for finding patterns in the large data sets.
Form clusters of input data.
Map output of clusters.
Given a new example, find cluster and generate into associated output.
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