Q3. By using XOR problem truth table calculate the output for 3 neurons ,then construct the completed decision boundary of network output of inputs i1 and i2, where each neuron uses threshold function as an activation function to point out the level 0 and 1? The attributes values are given below, where w11,w12, w21, w22, w31 and w32 are the weights values and b1, b2 and b3 are the bias values ? W11 = -1 w12= 2 bı= - 0.5 W21 = -2 w22 = -1 b2= 1.5 W31 = 1 w32 = -2 b3= 0.5

Q3. By using XOR problem truth table calculate the output for 3 neurons ,then construct the completed decision boundary of network output of inputs i1 and i2, where each neuron uses threshold function as an activation function to point out the level 0 and 1? The attributes values are given below, where w11,w12, w21, w22, w31 and w32 are the weights values and b1, b2 and b3 are the bias values ? W11 = -1 w12= 2 bı= - 0.5 W21 = -2 w22 = -1 b2= 1.5 W31 = 1 w32 = -2 b3= 0.5

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

See similar textbooks

Related questions

Q: Target 1 1 1 0 0 0 Prediction 0.9493117 0.8204206 0.4654966 0.2205311 0.9600351 0.050828 Considering…

A: Conversion Using threshold: Using threshold, machine learning algorithms interpret class labels from…

Q: Some models are more over-prone to overfitting than others. Why is it not enough to split a dataset…

A: Dear Student, The answer to your question is given below -

Q: Using KNN method, predict the most likely performance for a new part-time staff with personality…

A: KNN method in Machine Learning:"K-Nearest Neighbor" is represented by the acronym KNN. The algorithm…

Q: Principal Components Analysis, how might you decide how many components to keep and then input into…

A: The given question comes under multiple choice selected based question.

Q: Neurons compute? Neurons compute an activation function and a linear function (z = Wx + b). Neurons…

A: The correct statement is: Neurons compute a linear function (z = Wx + b) and an activation function.…

Q: uppose, you have trained a k-NN model and now you want to get the prediction on test data. Before…

A: How long a k-NN model takes to forecast relies on k and the amount of observations in your dataset.…

Q: Please use Housingdataset.csv file. Build a Transaction Type predictive model using 3 different…

A: KNN as a simple,effective and nonparametric prediction method.Its principle is to expand the area…

Q: Now that we have fit our model, which means that we have computed the optimal model parameters, we…

A: Linear regression analysis is used to predict the value of one variable based on the value of…

Q: To learn decision trees, assume we only include a feature in the model if its information again is…

A: Decision trees represent a supervised machine learning approach that can be employed for both…

Q: A) Compute and write the numerical value of the eigenvalue 14 of Σ. This eigenvalue is located in…

A: Given the variance-covariance matrix (Σ) for a centered dataset with (n=80) observations and (p=5)…

Q: Given the following methodology to train a model, explain what is wrong in the proposed methodology.…

A: The proposed methodology has a few issues. First, using only 640 images to train a model to predict…

Q: Assume there are three hypotheses, h1, h2, h3, which are trained from the same data set D. The…

A: To find the predicted result of the Bayes optimal classifier, we need to calculate the posterior…

Q: A learning curve is defined as: Ⓒa. A plot of the performance of the classifier on the y axis versus…

A: Learning curve is present in the machine learning. It also known as graphical record. In the…

Q: The table below provides a training data set containing six observations, three pre- dictors, and…

A: solution: given data: training data set containing six observations:- Given that data contains 6…

Q: train an artificial neural network using CIFAR10 dataset. You can get the dataset from Keras similar…

A: utilize the enhancers from this rundown: SGD, SGD with force, RMSProp and Adam I'm involving keras…

Q: Every information retrieval (IR) system is either directly or indirectly connected to a certain…

A: Information Retrieval (IR) is the dealing with the storage, retrieval and evaluation of information…

Q: Not all augmenting paths are equal, and starting with different paths leads to different residual…

A: Given that, Not all augmenting paths are equal, and starting with different paths leads to different…

Q: Suppose you are fitting a neural network with one hidden layer to a training set. You find that the…

A: You are just getting started on this project. What is the first thing you do? Assume each of the…

Q: In a multivariate model (ROC analysis), the area under the curve is 0.705 , explain how this is…

A: Consider the given predicted power graph:

Question

Q3. By using XOR problem truth table calculate the output for 3 neurons ,then construct the
completed decision boundary of network output of inputs i1 and i2, where each neuron uses
threshold function as an activation function to point out the level 0 and 1? The attributes
values are given below, where w11,w12, w21, w22, w31 and w32 are the weights values
and b1 , b2 and b3 are the bias values ?
Wi1= -1 w12= 2
bı= - 0.5
W21 = -2 w22= -1
b2= 1.5
W31 = 1 w32 = -2
b3= 0.5

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

This is a popular solution!

SEE SOLUTION Check out a sample Q&A here

Step 1

Step 2

Step 3

Step 4

Step 5

Step 6

Step 7

Step 8

Trending now

This is a popular solution!

Step by step

Solved in 8 steps with 8 images

SEE SOLUTION Check out a sample Q&A here

Similar questions

Using the Rectified Linear Unit (ReLU) non-linear transformation function in a single hidden layer neural network with 9 hidden units for a regression task. The third hidden unit has a linear predictor value equal to 5. What is the value of the derivative of the hidden unit response with respect to the linear predictor for the third hidden unit when the linear predictor is 3 with a ReLU activation function?
We would like to learn a model to predict whether a monster is scary or not (S). We identify four features for this purpose: Number of eyes (N), Color of eyes (C), Height (H), and Odor (O). We collect data 8 monster as shown below. Rank these four features according to their mutual information with S. Make sure to identify ties.
train an artificial neural network using CIFAR10 dataset. You can get the dataset from Keras similar to mnist dataset - try to find the best performing model for your dataset (CIFAR-10), use the splitting for train/val/test as 80/10/10 attach the screenshot of code and output
In Data and machin learning answer this:
Consider the following perceptron with the activation function f(I)=arctan(I-0.6). Use delta learning rule toupdate the weights w1 and w2 for the training data x=[1, 1] and D=1. (Choose k=0.2) it wont let me post the screenshot of the model but the w1 is 0.2 and w2 is 0.4
In Reid and Jamieson (2023) the authors had to use a low learning rate in their Minerva 2 model to successfully account for DRM effects. Describe what the learning rate in Minerva 2 does . Using the results from the above simulation describe why you believe it is necessary to use a low learning rate when accounting for DRM results using distributional models .
4. If the interaction in a two-way ANOVA is significant, what else can be concluded? a. neither main effect is significant b. one main effect is significant, but the other is not c. both main effects are significant d. none of the above
Suppose you are fitting a neural network with one hidden layer to a training set. You find that the cross validation error Jcv (0) is much larger than the training error Jtrain (0). Is increasing the number of hidden units likely to help? CV Yes, because it is currently suffering from high bias. Ob. Yes, because this increases the number of parameters and lets the network represent more complex functions. Oc. No, because it is currently suffering from high variance, so adding hidden units is unlikely to help. Od. No, because it is currently suffering from high bias, so adding hidden units is unlikely to help.
The accuracy in the output depends on the correct inputs. The model designed is supposedto be evaluated to determine the accuracy and it is supposed to function in a supervisedway. Hence the data sets can affect the model and vice versa.Given P(D|M) ,P(M) ,and P(D'|M), solve the followinga)Draw the probability tree for the main situationB)Draw the reverse tree for the main situation.C)Using the main tree, prove all probabilities on the opposite tree.D) If P(DIM) = 0.3,P(M) = 0.1 ,and P(D'IM) = 0.65 find P(D)
Question 48. Let us return to the Titanic data set. We now have learned several models and want to choose the best one. We used three different methods to validate these models: The training error rate (apparent error rate), the error rate on an external test set and the error rate estimated by a 10-fold cross validation. Training Error | Error on the test set | Cross Validation Error 0.18 Learner Decision Tree 0.22 0.21 Random Forest 0.01 0.10 0.12 1-Nearest-Neighbour 0.18 0.19 Which of the following statements are correct? a) 1-Nearest-Neighbour has a perfect training error and hence it should be used here. b) Random Forests outperforms both 1-Nearest-Neighbour and the Decision Tree in terms of prediction error. c) Not just in this case, but in general, Cross Validation is the better validation strategy and should always be preferred over the error on a single test set. d) Not just in this case, but in general, Decision Trees always perform worse than Random Forests.
Consider the decision model/game theoretic model from Chapter 19 for the spread of a new behavior through a social network. Suppose we have the social network depicted in Figure 2; suppose that each node starts with the behavior B, and each node has a threshold of q = 2/5 for switching to behavior A. Now, let c and d form a two-node set S of initial adopters of behavior A. If other nodes follow the threshold rule for choosing behaviors, which nodes will eventually switch to A? Give a brief (1-2 sentence) explanation for your answer. Find a cluster of density greater than 1 − q = 3/5 in the part of the graph outside S that blocks behavior A from spreading to all nodes, starting from S, at threshold q. Give a brief (1-2 sentence) explanation for your answer. Suppose you were allowed to add a single edge to the given network, connecting one of nodes c or d to any one node that it is not currently connected to. Could you do this in such a way that now behavior A, starting from S and…
2. Can you design a binary classification experiment with 100 total population (TP+TN+FP+ FN), with precision (TP/(TP+FP)) of 1/2, with sensitivity (TP/(TP+FN)) of 2/3, and specificity (TN/(FP+TN)) of 3/5? (Please consider the population to consist of 100 individuals.)