What is the other way to reduce error in recall due to false minima apart from stochastic update?

What is the other way to reduce error in recall due to false minima apart from stochastic update? Correct Answer by storing desired patterns at lowest energy minima

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

Can error in recall due to false minima be reduced?
How can error in recall due to false minima be reduced?
How can error in recall due to false minima be further reduced?
You are the network administrator for your company. The company has numerous branch offices, and each office uses Internet Connecting Sharing to connect to the Internet. A new employee named David Johnson is configuring a Windows 2000 Server computer as a file server. When David uses Windows update for the first time and select Product Update, he receives an error message stating that access is denied. David needs to be able to update the file by using his account. What should you do?
How can false minima be reduced in case of error in recall in feedback neural networks?
Presence of false minima will have what effect on probability of error in recall?
Why there is error in recall, when number of energy minima is more the required number of patterns to be stored?
Stochastic gradient descent has the possibility of escaping from local minima.
Imagine, you are solving a classification problems with highly imbalanced class. The majority class is observed 99% of times in the training data. Your model has 99% accuracy after taking the predictions on test data. Which of the following is true in such a case?
1. Accuracy metric is not a good idea for imbalanced class problems.
2.Accuracy metric is a good idea for imbalanced class problems.
3.Precision and recall metrics are good for imbalanced class problems.
4.Precision and recall metrics aren't good for imbalanced class problems.
As temperature increase, what happens to stochastic update?