- What is the objective of pattern mapping problem?
- Which is one of the application of associative memories?
- How can false minima be reduced?
- What is Backpropagation Sanfoundry?
- How do you identify a pattern?
- What is pattern recognition with example?
- What is objective of linear Autoassociative feedforward networks?
- What is the objective of backpropagation algorithm?
- Why do we need biological neural networks?
- What is accretive Behaviour?
- How can error in recall due to false minima be reduced?
- Why is pattern recognition important?

## What is the objective of pattern mapping problem?

Explanation: The objective of pattern mapping problem is to capture implied function.

5.

To provide generalization capability to a network, what should be done.

Explanation: To provide generalization capability to a network, except input layer, all units in other layers should be non – linear..

## Which is one of the application of associative memories?

7. Which is one of the application of associative memories? Explanation: The objective of associative memories is to store association between patterns for later recall of one of patterns given the other, so noisy versions of the same image can be recalled. 8.

## How can false minima be reduced?

Explanation: Presence of false minima will increase the probability of error in recall. Explanation: Presence of false minima can be reduced by stochastic update.

## What is Backpropagation Sanfoundry?

Explanation: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn. … Explanation: Linearly separable problems of interest of neural network researchers because they are the only class of problem that Perceptron can solve successfully.

## How do you identify a pattern?

Recognizing a pattern is like looking through a telescope for the first time. As if with new eyes, you see things that you have never seen before. That same experience can happen when you see a pattern for the first time.

## What is pattern recognition with example?

An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is “spam” or “non-spam”). However, pattern recognition is a more general problem that encompasses other types of output as well.

## What is objective of linear Autoassociative feedforward networks?

Explanation: The objective of linear autoassociative feedforward networks is to associate a given pattern with itself.

## What is the objective of backpropagation algorithm?

Explanation: The objective of backpropagation algorithm is to to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly.

## Why do we need biological neural networks?

Why do we need biological neural networks? Explanation: These are the basic aims that a neural network achieve. … Explanation: Humans have emotions & thus form different patterns on that basis, while a machine(say computer) is dumb & everything is just a data for him.

## What is accretive Behaviour?

Explanation: In accretive behaviour, pattern lying closet to the desired pattern is recalled. … Explanation: There are only two layers & single set of weights in pattern association.

## How can error in recall due to false minima be reduced?

Explanation: Error in recall due to false minima can be reduced by stochastic update or by storing desired patterns at lowest energy minima.

## Why is pattern recognition important?

Regular Expressions are helpful in identifying complex text patterns for natural language processing. Pattern recognition is used to give human recognition intelligence to machines which are required in image processing. applications like biological and biomedical imaging.