The most common way I've seen is to have a slide where you have the additional information. It can be phone numbers, email address, websites, books, anything you want to refer to.
People tend to photograph these slides so make sure the information is error free and that you add you own marketing info to the slide as well.
Answer:
The answer is nearest-neighbor learning.
because nearest neighbor learning is classification algorithm.
It is used to identify the sample points that are separated into different classes and to predict that the new sample point belongs to which class.
it classify the new sample point based on the distance.
for example if there are two sample points say square and circle and we assume some center point initially for square and circle and all the other points are added to the either square or circle cluster based on the distance between sample point and center point.
while the goal of decision tree is to predict the value of the target variable by learning some rules that are inferred from the features.
In decision tree training data set is given and we need to predict output of the target variable.
Explanation:
Answer:
Check the explanation
Explanation:
When it comes to the field of computer science and information theory, the Huffman code is a specific type of optimal prefix code that is mostly utilized for the compression of lossless data. The process and procedures of finding or using such a code proceeds by means of Huffman coding, which is an algorithm that was developed by David A.
kindly check the below image for the complete answer to your question.
Answer:
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Explanation:
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Answer:
Port Number
Explanation:
In the transport Layer of the OSI Model the port number is used to identify the receiving application. Temporary communication sessions are set up that enable data exchange, the two protocols used are Transmission Control Protocol(TCP) and the User Datagram Protocol(UDP) . Each port is assigned a number that identifies it according to its function. The range for some well-known ports is 0-1023.
