[LO 5 & LO 6] Train by hand a single neuron using perceptron learning rules on the training set given below. Assume that all initial weights including the bias of the neuron are zeros (0). Show the set of weights including the bias at the end of each iteration. Use learning rate =1 and %3D the threshold function_h»(X) : threshold(net) = 1 if net >= 0; O otherwise (pls check it out, textbook AIMA 3rd threshold(net) Edition pp. 723-724). Apply the examples in the given order and stop the iteration by the time when you find the patterns are all correctly classified. Is the training set linearly separable, give your comment on Expl. No it. Input 100 011 110 Output 1 1 4 111 001 6. 101 1 Learning Outcomes: LO 5: Apply various techniques to an agent when acting under certainty LO 6 : Apply various AI algorithms to solve the problems

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
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Please answer the questions in the picture correctly, and it is done by a team of bartleby experts. Please don't copy paste the answer from the website, because it was my previous question. Not purely answered by a bartlely team expert, but rather taking Chegg's answer. So, please don,t copy paste solution from website. Because many answers from websites are inaccurate, as well as from Chegg

[LO 5 & LO 6] Train by hand a single neuron using perceptron learning
rules on the training set given below. Assume that all initial weights
including the bias of the neuron are zeros (0). Show the set of weights
including the bias at the end of each iteration. Use learning rate =1 and
the threshold function h(X) : threshold(net) = 1 if net >= 0;
threshold(net) = 0 otherwise (pls check it out, textbook AIMA 3rd
Edition pp. 723-724). Apply the examples in the given order and stop
the iteration by the time when you find the patterns are all correctly
classified. Is the training set linearly separable, give your comment on
Expl. No
Output
it.
Input
100
1
2
011
110
111
4
001
6
101
Learning Outcomes:
LO 5: Apply various techniques to an agent when acting under
certainty
LO 6: Apply various AI algorithms to solve the problems
Transcribed Image Text:[LO 5 & LO 6] Train by hand a single neuron using perceptron learning rules on the training set given below. Assume that all initial weights including the bias of the neuron are zeros (0). Show the set of weights including the bias at the end of each iteration. Use learning rate =1 and the threshold function h(X) : threshold(net) = 1 if net >= 0; threshold(net) = 0 otherwise (pls check it out, textbook AIMA 3rd Edition pp. 723-724). Apply the examples in the given order and stop the iteration by the time when you find the patterns are all correctly classified. Is the training set linearly separable, give your comment on Expl. No Output it. Input 100 1 2 011 110 111 4 001 6 101 Learning Outcomes: LO 5: Apply various techniques to an agent when acting under certainty LO 6: Apply various AI algorithms to solve the problems
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