{"id":17585,"date":"2024-04-15T05:15:16","date_gmt":"2024-04-15T05:15:16","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=17585"},"modified":"2024-04-15T05:15:16","modified_gmt":"2024-04-15T05:15:16","slug":"why-are-linearly-separable-problems-of-interest-of-neural-network-researchers-a-because-they-are-the-only-class-of-problem-that-network-can-solve-successfully-b-because-they-are-the-only-class-of-p","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/why-are-linearly-separable-problems-of-interest-of-neural-network-researchers-a-because-they-are-the-only-class-of-problem-that-network-can-solve-successfully-b-because-they-are-the-only-class-of-p\/","title":{"rendered":"Why are linearly separable problems of interest of neural network researchers? A. Because they are the only class of problem that network can solve successfully B. Because they are the only class of problem that Perceptron can solve successfully C. Because they are the only mathematical functions that are continue D. Because they are the only mathematical functions you can draw"},"content":{"rendered":"

[amp_mcq option1=”Because they are the only class of problem that network can solve successfully” option2=”Because they are the only class of problem that Perceptron can solve successfully” option3=”Because they are the only mathematical functions that are continue” option4=”Because they are the only mathematical functions you can draw” correct=”option2″]<\/p>\n

The correct answer is: B. Because they are the only class of problem that Perceptron can solve successfully<\/strong>.<\/p>\n

A Perceptron is a type of artificial neural network that can only learn to classify linearly separable data. This means that the data must be able to be separated into two classes by a straight line. If the data is not linearly separable, then a Perceptron will not be able to learn to classify it correctly.<\/p>\n

Neural network researchers are interested in linearly separable problems because they are a relatively simple type of problem that can be used to test the performance of neural networks. If a neural network can learn to classify linearly separable data correctly, then it is more likely to be able to learn to classify more complex data correctly.<\/p>\n

Options A, C, and D are incorrect because they are not true. Neural networks can solve problems that are not linearly separable, and they can also solve problems that are not mathematical functions.<\/p>\n","protected":false},"excerpt":{"rendered":"

[amp_mcq option1=”Because they are the only class of problem that network can solve successfully” option2=”Because they are the only class of problem that Perceptron can solve successfully” option3=”Because they are the only mathematical functions that are continue” option4=”Because they are the only mathematical functions you can draw” correct=”option2″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[676],"tags":[],"class_list":["post-17585","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","no-featured-image-padding"],"yoast_head":"\nWhy are linearly separable problems of interest of neural network researchers? A. Because they are the only class of problem that network can solve successfully B. Because they are the only class of problem that Perceptron can solve successfully C. 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