Imagine we apply the perceptron learning algorithm to the 5 point data set we used in the above problem: Negative: (1, 0) (2, 1) (2, 2), Positive: (0, 0) (2, 0). What would be the result of such classification? A) The perceptron algorithm would converge since the 5 point data set is linearly separable. B) The perceptron algorithm would not converge since the 5 point data set is linearly separable. C) The perceptron algorithm would converge since the 5 point data set is not linearly separable. D) The perceptron algorithm would not converge since the 5 point data set is not linearly separable.

Imagine we apply the perceptron learning algorithm to the 5 point data set we used in the above problem: Negative: (1, 0) (2, 1) (2, 2), Positive: (0, 0) (2, 0). What would be the result of such classification? A) The perceptron algorithm would converge since the 5 point data set is linearly separable. B) The perceptron algorithm would not converge since the 5 point data set is linearly separable. C) The perceptron algorithm would converge since the 5 point data set is not linearly separable. D) The perceptron algorithm would not converge since the 5 point data set is not linearly separable.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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