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JC · 2005-10-21, 09:50 AM

Answer:

The Neural Net is uninteresting because, *no matter* how large and complicated it is, it can be replaced by a single perceptron (node) for each output.

Training a single node is far faster than training 500!

A single perceptron here represents a linear combination of the weighted inputs. Without thresholds, the output will always just be a linear combination of the weighted inputs.

The perceptron can represent a vector in an N dimensional space, where N is the number of inputs. If you add two/three/a billion vectors together you just get another vector.

So the entire Net only has the power to represent a vector for each of its outputs.

This means that it can be replaced by a single node for each output with no loss of representational ability.

2005-10-21, 09:50 AM	#13
JC Ask me about dubs! Real Name: JC Join Date: Dec 2002 Location: Orange, CA Posts: 2,895 Car: 2013 Triumph Speed Triple R	Answer: The Neural Net is uninteresting because, no matter how large and complicated it is, it can be replaced by a single perceptron (node) for each output. Training a single node is far faster than training 500! A single perceptron here represents a linear combination of the weighted inputs. Without thresholds, the output will always just be a linear combination of the weighted inputs. The perceptron can represent a vector in an N dimensional space, where N is the number of inputs. If you add two/three/a billion vectors together you just get another vector. So the entire Net only has the power to represent a vector for each of its outputs. This means that it can be replaced by a single node for each output with no loss of representational ability. __________________ Actually, I am a rocket scientist.