The main aim of running the parameters in the linear perceptron algorithm is to be able to develop a machine learning algorithm for binary classification tasks.
<h3>What is a linear perceptron algorithm?</h3>
This refers to the linear classification algorithm that is used in machine learning.
This is done in order to learn a decision boundary that divides different classes using a hyperplane.
Hence, we can see that your question is incomplete because the parameters are not included, hence a general overview was given to give you a better understanding of the concept.
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Answer:
Following are the response to the given question:
Explanation:
The glamorous objective is to examine the items (as being the most valuable and "cheapest" items are chosen) while no item is selectable - in other words, the loading can be reached.
Assume that such a strategy also isn't optimum, this is that there is the set of items not including one of the selfish strategy items (say, i-th item), but instead a heavy, less valuable item j, with j > i and is optimal.
As
, the i-th item may be substituted by the j-th item, as well as the overall load is still sustainable. Moreover, because
and this strategy is better, our total profit has dropped. Contradiction.
Answer:
I think the best option would be C. Marked
Explanation:
hope this helps and sorry if it is incorrect.
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