1-Please describe about overfitting in MLP. 2- Please describe about the number of neurons in input layer and output layer. Also, please describe about the winner neuron in SOM. Which neuron is winner in SOM? ( 3- Please describe about kemel in nonlinear SVM. And also, please describe about the role of Kernel in nonlinear SVM. Also, please illustrate a figure for describing the role of kemel in nonlinear SVM. ( 4- What is different between clustering and classification? (

1-Please describe about overfitting in MLP. 2- Please describe about the number of neurons in input layer and output layer. Also, please describe about the winner neuron in SOM. Which neuron is winner in SOM? ( 3- Please describe about kemel in nonlinear SVM. And also, please describe about the role of Kernel in nonlinear SVM. Also, please illustrate a figure for describing the role of kemel in nonlinear SVM. ( 4- What is different between clustering and classification? (

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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Exercise 10: Assume that you are trying to solve a binary classification problem with a feed-forward neural network. Assume that the training set has 1250 observations and 5 input features. Finally, assume that the network has only one layer of hidden neurons, and that layer contains 3 neurons. What is the total number of connections of the network? (Remark: to answer this question, you have to take into account also the threshold connections of each neuron). Answer 1. 9 Answer 2. 5 Answer 3. 22 Answer 4. 18 Answer 5. 1255 Answer 6. 3 Answer 7. 1262 Answer 8. None of the other alternatives. Answer 9. 13 Answer 10. 1250 Answer 11. 20 Answer 12. 1258
P9.4 The operational amplifier-resistor neuron circuit from Figure P9.4(a) needs to be analyzed. Compute fo2 as a function of w₁, W₂, and w3, and of the bias T as indicated on the neuron symbol of Figure 9.4(b). Assume for computation purposes that the two neurons are equivalent. 600 W 30002 W 180002 9000 ww 12 k ww Www 18000 H 3 Xxz X4 =-4V X₁ X₂ Xy W₁ W₂ for (a) 12000 www for -f(x-7) W3 T -1 (b) Figure P9.4 High-gain neuron with weights and nonzero threshold: (a) circuit diagram and (b) symbol. (a) Draw the neuron's transfer characteristics as a function of z= Wix. (b) Determine the range of activation z that allows for the linear operation of the neuron. Assume fat-| = |fsat+= 14 V. (c) Assume that the neuron needs to classify patterns of two classes and find its responses due to the eight individual input values, which are vertices of a three-dimensional [-1,1] cube.
About Perceptron networks, mark the correct alternative considering true (T) or false (F). ( ) May have one or more hidden layers. ( ) Some authors use the name “Perceptron” to refer to a small network of a single artificial neuron (LTU). ( ) It can be defined as a feedforward network. ( ) Can be modified to solve linear regression tasks. AF, T, T, T. B.F, F, T, T. C.T, T, T, T. d.F, T, T, F. and.V, F, F, V.
Incorrect Question 1 Refer to the following network flow and state the min cut, of the graph G. -2/2 3/7 6/6 -5/5 (s.v1),(s,v3).(s.v2) 1/1 O (v1,v4), (v1,v3),(s,v3),(s,v2) (v4,t).(v3,t).(v5,t) 0/3 (s,v2).(v2,v3),(v2,v5) -6/9 4/5 -2/3 6/6 6/8
In deep learning, given a multilayer perceptron, do weight matrices contain parameters to map values from a source layer (except for the input one) to a destination layer and do bias vectors contain parameters associated with destination layers only, (i.e. one bias vector per destination layer)?
What is the purpose of an activation function applied to an artificial neuron? -To add non-linear transformation to the neuron output -To rescale the output of the neuron -To normalize outputs of the neurons on a single hidden layer -To prepare output of the neuron for max/min/ave pooling -To perform batch normalization select one only
1: A four-layer neural network is to have five inputs, four nodes for first hidden layer, three nodes for second hidden layer, and six outputs. The range of the outputs is to be continuous between 0 and 1. a. How many neurons are required for this network? b. What are the dimensions of the weight matrices? c. What kinds of transfer functions can be used in each layer? d. Are biases required in any layer? XI X₂ X3 -1 2 0 -1 1 0 1 -2 2 8=0) 9=+1 -1
2. Consider the following hierarchical state machine: input: a: pure output: b: pure a/b a/b D a/b -1- HB a/b a/ F Construct an equivalent flat FSM giving the semantics of the hierarchy. Describe in words the input/output behavior of this machine. Is there a simpler machine that exhibits the same behavior?
Assume that we want a two-layer neural network with four inputs and six outputs. The outputs range should be continuous between 0 and 1. What would be the best network architecture for this situation? Define the following characteristics of this network architecture: I. The number of required neurons in each layer. II. The dimensions of weight matrices in the first and second layers III. The transfer functions in each layer