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Soft Computing: Neural Network 1
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Soft Computing: Neural Network 1
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25 Questions

1. Online learning allows network to incrementally adjust weights continuously?
2. What is the full form of BN in Neural Networks?
3. Ability to learn how to do tasks based on the data given for training or initial experience
4. Supervised learning may be used for?
5. In pattern mapping problem in neural nets, is there any kind of generalization involved between input & output?
6. What is the condition in Stochastic models, if xb(t) represents differentiation of state x(t)?
7. Artificial neural network used for
8. Feature of ANN in which ANN creates its own organization or representation of information it receives during learning time is
9. Neurons or artificial neurons have the capability to model networks of original neurons as found in brain
10. What is probablistic credit assignment?
11. How is pattern information distributed?
12. If xb(t) represents differentiation of state x(t), then a stochastic model can be represented by?
13. What is plasticity in neural networks?
14. The output at each node is called_____.
15. Does pattern classification & grouping involve same kind of learning?
16. Drawbacks of template matching are?
17. What is structural learning?
18. Adjustments in activation is slower than that of synaptic weights?
19. In artificial Neural Network interconnected processing elements are called
20. Does for feature mapping there's need of supervised learning?
21. Whats true for sparse encoding learning?
22. Why do we need biological neural networks?
23. What is temporal learning?
24. What is nature of input in activation dynamics?
25. What is fixed credit assignment?

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