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Welcome to the Learning MCQs Page

Dive deep into the fascinating world of Learning with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Learning, a crucial aspect of Artificial Intelligence. In this section, you will encounter a diverse range of MCQs that cover various aspects of Learning, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Artificial Intelligence.

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Check out the MCQs below to embark on an enriching journey through Learning. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Artificial Intelligence.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Learning. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Learning MCQs | Page 7 of 9

Explore more Topics under Artificial Intelligence

Q61.
Typical text mining tasks include?

a.

text categorization

b.

text clustering

c.

entity relation modeling

d.

All of the above

Discuss
Answer: (d).All of the above
Q62.
Which of the following technique is not a part of flexible text matching?

a.

Soundex

b.

Metaphone

c.

Keyword Hashing

d.

Edit Distance

Discuss
Answer: (c).Keyword Hashing
Q63.
What is the right order for a text classification model components?

a.

Text cleaning -> Text annotation -> Gradient descent -> Model tuning -> Text to predictors

b.

Text cleaning -> Text annotation -> Text to predictors -> Gradient descent -> Model tuning

c.

Text cleaning -> Gradient descent -> Model tuning -> Text to predictors -> Text annotation

d.

Text cleaning -> Text annotation -> Model tuning -> Text to predictors -> Gradient descent

Discuss
Answer: (b).Text cleaning -> Text annotation -> Text to predictors -> Gradient descent -> Model tuning
Q64.
What are the possible features of a text corpus?

a.

Count of word in a document

b.

Vector notation of word

c.

Part of Speech Tag

d.

All of the above

Discuss
Answer: (d).All of the above
Q65.
What is the major difference between CRF (Conditional Random Field) and HMM (Hidden Markov Model)?

a.

CRF is Generative whereas HMM is Discriminative model

b.

Both CRF and HMM are Generative model

c.

CRF is Generative whereas HMM is Discriminative model

d.

Both CRF and HMM are Discriminative mode

Discuss
Answer: (c).CRF is Generative whereas HMM is Discriminative model
Q66.
Stemming: This refers to the process of separating the prefixes and suffixes from words to derive the root word form and meaning.

a.

True

b.

False

c.

Can be true or false

d.

Can't Say

Discuss
Answer: (a).True
Q67.
Production/Rule looks like________________.

a.

pattern-->data

b.

action-->data

c.

pattern-->action

d.

none of the above

Discuss
Answer: (c).pattern-->action
Q68.
How can we convert AO graph with mixed nodes into graph with pure AND and OR nodes?

a.

by traversing multiple node

b.

by deleting one of the node

c.

by addition of extra node

d.

none of the above

Discuss
Answer: (c).by addition of extra node
Q69.
Arc consistency in AO graph is concernd with ____________________________________.

a.

nodes

b.

finding consistent values for pairs of variables.

c.

unary constraint

d.

all of the above

Discuss
Answer: (b).finding consistent values for pairs of variables.
Q70.
A planning problem P in BSSP is defined as a _____________.

a.

triple (s, g, o)

b.

triple (s1, s2, o)

c.

triple (g1, g, o)

d.

none of the above

Discuss
Answer: (a).triple (s, g, o)
Page 7 of 9

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