Match the LIST-I with LIST-II

LIST - I

LIST - II

A.

Decision Tree

I.

Delta Learning Rule

B.

Supervised Learning

II.

Self Organizing Map

C.

Artificial Neural Network

III.

C4.5 Algorithm

D.

Instance base Learning

IV.

Non-linear Regression Algorithm


Choose the correct answer from the options given below:

  1. A - I, B - II, C - III, D - IV 
  2. A - II, B - III, C - IV, D - I 
  3. A - III, B - IV, C - I, D - II 
  4. A - IV, B - I, C - II, D - III

Answer (Detailed Solution Below)

Option 3 : A - III, B - IV, C - I, D - II 

Detailed Solution

Download Solution PDF
```html Matching LIST-I with LIST-II - www.guacandrollcantina.com

Match the LIST-I with LIST-II

LIST - I LIST - II
A. Decision Tree III. C4.5 Algorithm
B. Supervised Learning IV. Non-linear Regression Algorithm
C. Artificial Neural Network I. Delta Learning Rule
D. Instance base Learning II. Self Organizing Map

The correct answer is Option 3.

Key Points

  • Decision Tree (A) is linked with the C4.5 Algorithm (III) which is a well-known algorithm used to generate a decision tree.
  • Supervised Learning (B) can be associated with Non-linear Regression Algorithm (IV) as it involves using labeled data to predict outcomes.
  • Artificial Neural Network (C) is connected to the Delta Learning Rule (I) which is used to adjust the weights in the network.
  • Instance base Learning (D) relates to the Self Organizing Map (II) which is a type of artificial neural network that uses unsupervised learning to produce a low-dimensional representation of the input space.

Additional Information

  • The C4.5 algorithm is an extension of the earlier ID3 algorithm and is used for generating a decision tree based on labeled input data.
  • Supervised learning algorithms, including non-linear regression, are used for making predictions based on input-output pair datasets.
  • The Delta Learning Rule is a gradient descent learning rule used for training artificial neural networks.
  • Self Organizing Maps are used for clustering and visualizing high-dimensional data in a low-dimensional space.
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