Why is the XOR problem exceptionally interesting to neural network researchers?

a) Because it can be expressed in a way that allows you to use a neural network

b) Because it is complex binary operation that cannot be solved using neural networks

c) Because it can be solved by a single layer perceptron

d) Because it is the simplest linearly inseparable problem that exists.

1 Answer

Answer :

d) Because it is the simplest linearly inseparable problem that exists.

Related questions

Description : This set of Artificial Intelligence MCQs focuses on Neural Networks - 2 . 1. Why is the XOR problem exceptionally interesting to neural network researchers? a) Because it can be expressed in ... by a single layer perceptron d) Because it is the simplest linearly inseparable problem that exists.

Last Answer : d) Because it is the simplest linearly inseparable problem that exists.

Description : Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results. ... have to be explicitly hand-coded d) False - just having a single perceptron is enough

Last Answer : c) True – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded

Description : Why are linearly separable problems of interest of neural network researchers? a) Because they are the only class of problem that network can solve successfully b) Because they are the only ... mathematical functions that are continue d) Because they are the only mathematical functions you can draw

Last Answer : b) Because they are the only class of problem that Perceptron can solve successfully

Description : What is perceptron? a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

Last Answer : a) a single layer feed-forward neural network with pre-processing

Description : A perceptron is: a) a single layer feed-forward neural network with pre-processing b) an auto-associative neural network c) a double layer auto-associative neural network d) a neural network that contains feedback

Last Answer : a) a single layer feed-forward neural network with pre-processing

Description : Perceptron is A. General class of approaches to a problem. B. Performing several computations simultaneously C. Structures in a database those are statistically relevant D. Simple forerunner of modern neural networks, without hidden layers

Last Answer : D. Simple forerunner of modern neural networks, without hidden layers

Description : The network that involves backward links from output to the input and hidden layers is called _________ a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron

Last Answer : c) Recurrent neural network

Description : A perceptron is a ______________ a) Feed-forward neural network b) Backpropagation algorithm c) Backtracking algorithm d) Feed Forward-backward algorithm

Last Answer : a) Feed-forward neural network

Description : A perceptron is a ——————————–. a) Feed-forward neural network b) Back-propagation algorithm c) Back-tracking algorithm d) Feed Forward-backward algorithm e) Optimal algorithm with Dynamic programming

Last Answer : a) Feed-forward neural network

Description : The network that involves backward links from output to the input and hidden layers is called as ____. a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron

Last Answer : c) Recurrent neural network

Description : Which of the following is true for neural networks? (i) The training time depends on the size of the network. (ii) Neural networks can be simulated on a conventional computer. (iii) Artificial neurons are identical in ... b) (ii) is true c) (i) and (ii) are true d) None of the mentioned

Last Answer : c) (i) and (ii) are true

Description : Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in ... ) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

Last Answer : a) (ii) and (iii) are true

Description : Which of the following is true? Single layer associative neural networks do not have the ability to: (i) perform pattern recognition (ii) find the parity of a picture (iii)determine whether two or more shapes in a ... ) are true b) (ii) is true c) All of the mentioned d) None of the mentioned

Last Answer : a) (ii) and (iii) are true

Description : Which of the following neural networks uses supervised learning? (A) Multilayer perceptron (B) Self organizing feature map (C) Hopfield network (1) (A) only (2) (B) only (3) (A) and (B) only (4) (A) and (C) only

Last Answer : (1) (A) only

Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : a) Linear Functions

Description : Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : b) Nonlinear Functions

Description : Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions

Last Answer : b) Nonlinear Functions

Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : a) Linear Functions

Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

Last Answer : d) All of the mentioned

Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned

Last Answer : d) All of the mentioned

Description : What is an auto-associative network? a) a neural network that contains no loops b) a neural network that contains feedback c) a neural network that has only one loop d) a single layer feed-forward neural network with pre-processing

Last Answer : b) a neural network that contains feedback

Description : An auto-associative network is: a) a neural network that contains no loops b) a neural network that contains feedback c) a neural network that has only one loop d) a single layer feed-forward neural network with pre-processing

Last Answer : b) a neural network that contains feedback

Description : What is single layer feed forward neural network? 

Last Answer : A network with all the inputs connected directly to the outputs is called a single layer neural network or a perceptron networks.

Description : Research scientists all over the world are taking steps towards building computers with circuits patterned after the complex interconnections existing among the human brain's nerve cells. What name ... ? a) Intelligent computers b) Supercomputers c) Neural network computers d) Smart computers

Last Answer : c) Neural network computers

Description : There exists two way to infer using semantic networks in which knowledge is represented as Frames. 1) Intersection Search 2) Inheritance Search a) True b) False

Last Answer : 1) Intersection Search

Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned

Last Answer : a) All of the mentioned are true

Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned

Last Answer : a) All of the mentioned are true

Description : ______________is a branch of science that deals with programing the systems in such a way that they automatically learn and improve with experience A. Machine Learning B. Deep Learning C. Neural Networks D. None of these

Last Answer : A. Machine Learning 

Description : Computer programs that mimic the way the human brain processes information is called as A. Machine Learning B. Deep Learning C. Neural Networks D. None of these

Last Answer : The correct answer is: C. Neural Networks Neural Networks are computer programs or models that are designed to mimic the way the human brain processes information. They are a subset of ... advanced form of neural networks that uses multiple layers of artificial neurons to perform complex tasks.

Description : Which is true regarding BFS (Breadth First Search)? a) BFS will get trapped exploring a single path b) The entire tree so far been generated must be stored in BFS c) BFS is not guaranteed to find a solution if exists d) BFS is nothing but Binary First Search

Last Answer : b) The entire tree so far been generated must be stored in BFS

Description : In ____ the goal is for the software to use what it has learned in one area to solve problems in other areas. A. Machine Learning B. Deep Learning C. Neural Networks D. None of these

Last Answer : B. Deep Learning 

Description : What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the ... the network to allow weights to be adjusted so that the network can learn d) None of the mentioned

Last Answer : c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn

Description : What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the ... network to allow weights to be adjusted so that the network can learn. d) None of the mentioned

Last Answer : c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.

Description : A _________ is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. a) Decision tree b) Graphs c) Trees d) Neural Networks

Last Answer : a) Decision tree

Description : Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Core of soft Computing is A. Fuzzy Computing, Neural Computing, Genetic Algorithms B. Fuzzy Networks and Artificial Intelligence C. Artificial Intelligence and Neural Science D. Neural Science and Genetic Science

Last Answer : D. Neural Science and Genetic Science

Description : Which of the following is the model used for learning? a) Decision trees b) Neural networks c) Propositional and FOL rules d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned

Last Answer : d) All of the mentioned

Description : What is multilayer feed forward neural networks?

Last Answer : It consists of many hidden units. Each hidden unit act as a perceptron that represents a soft threshold functions in the input space. Output unit act as a soft threshold linear combination of several such functions.

Description : What are the two functions in Neural network’s Activation functions? 

Last Answer : i. Threshold function  ii. Sigmoid function

Description : Define Neural Networks.

Last Answer : It consists of nodes or units connected by directed links. A link propagates the activation. Each link has a numeric weight which determines the strength and sign of the connection.

Description : Deep learning is a subfield of machine learning where concerned algorithms are inspired by the structured and function of the brain called _____. A. Machine learning B. Artificial neural networks C. Deep learning D. Robotics

Last Answer : B. Artificial neural networks 

Description : Claude Shannon described the operation of electronic switching circuits with a system of mathematical logic called _____________ a) LISP b) XLISP c) Neural networking d) Boolean algebra

Last Answer : c) Neural networking

Description : Which of the following is an application of NN (Neural Network)? a) Sales forecasting b) Data validation c) Risk management d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Which of the following is not the promise of artificial neural network? a) It can explain result b) It can survive the failure of some nodes c) It has inherent parallelism d) It can handle noise

Last Answer : a) It can explain result

Description : What is Neuro software? a) A software used to analyze neurons b) It is powerful and easy neural network

Last Answer : b) It is powerful and easy neural network

Description : An Artificial Neural Network Is based on? a) Strong Artificial Intelligence approach b) Weak Artificial Intelligence approach c) Cognitive Artificial Intelligence approach d) Applied Artificial Intelligence approach

Last Answer : c) Cognitive Artificial Intelligence approach

Description : Three main basic features involved in characterizing membership function are A. Intution, Inference, Rank Ordering B. Fuzzy Algorithm, Neural network, Genetic Algorithm C. Core, Support , Boundary D. Weighted Average, center of Sums, Median

Last Answer : C. Core, Support , Boundary

Description : In artificial Neural Network interconnected processing elements are called A. nodes or neurons B. weights C. axons D. Soma

Last Answer : A. nodes or neurons

Description : Artificial neural network used for A. Pattern Recognition B. Classification C. Clustering D. All of these

Last Answer : D. All of these