Given three data points and their labels: Negative: (-1, 0) (2, -2) Positive: (1, O). Assume we haveno bias term and we initialize the weight vector with all zero. We run the perceptron learningalgorithm by attempting to make predictions on (-1, 0) and (2, -2).Q5.1 Perceptron 1What is the weight vector learned by the perceptron learning algorithm after visiting (-1,0)?O (1, 0)O (2, 1)O (-2, 1)O (1, 2)Q5.2 Perceptron 2What is the weight vector learned by the perceptron learning algorithm after visiting (-1,0) and(2,-2)?O (1, 0)O (2, 1)O (-2, 1)O (-1, 2)

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Answered: Q5.1 Perceptron 1 What is the weight…

Q5.1 Perceptron 1 What is the weight vector learned by the perceptron learning algorithm after visiting (-1,0)? O (1, 0) O (2, 1) O (-2, 1) O (-1, 2)

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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Concept explainers

Eight puzzle problem

The N-puzzle problem contains N tiles (N+1 including empty tiles) where the value of N can be 8, 15, 24, etc. The puzzle is divided into the square root of (N+1) rows and the square root of (N+1) columns. These problems have an initial state or configuration and a goal state or configuration. The puzzle can be solved by shifting the tiles one by one in the empty space. The objective is to place the numbers on the tiles to match the final state using the empty space. The other name for the N-puzzle problem is the sliding puzzle.

Question

Given three data points and their labels: Negative: (-1, 0) (2, -2) Positive: (1, O). Assume we have
no bias term and we initialize the weight vector with all zero. We run the perceptron learning
algorithm by attempting to make predictions on (-1, 0) and (2, -2).
Q5.1 Perceptron 1
What is the weight vector learned by the perceptron learning algorithm after visiting (-1,0)?
O (1, 0)
O (2, 1)
O (-2, 1)
O (1, 2)
Q5.2 Perceptron 2
What is the weight vector learned by the perceptron learning algorithm after visiting (-1,0) and
(2,-2)?
O (1, 0)
O (2, 1)
O (-2, 1)
O (-1, 2)

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