Computer Science – Learning
Scientific paper
2008-11-02
Computer Science
Learning
Scientific paper
In this paper, I expand Shannon's definition of entropy into a new form of entropy that allows integration of information from different random events. Shannon's notion of entropy is a special case of my more general definition of entropy. I define probability using a so-called performance function, which is de facto an exponential distribution. Assuming that my general notion of entropy reflects the true uncertainty about a probabilistic event, I understand that our perceived uncertainty differs. I claim that our perception is the result of two opposing forces similar to the two famous antagonists in Chinese philosophy: Yin and Yang. Based on this idea, I show that our perceived uncertainty matches the true uncertainty in points determined by the golden ratio. I demonstrate that the well-known sigmoid function, which we typically employ in artificial neural networks as a non-linear threshold function, describes the actual performance. Furthermore, I provide a motivation for the time dilation in Einstein's Special Relativity, basically claiming that although time dilation conforms with our perception, it does not correspond to reality. At the end of the paper, I show how to apply this theoretical framework to practical applications. I present recognition rates for a pattern recognition problem, and also propose a network architecture that can take advantage of general entropy to solve complex decision problems.
No associations
LandOfFree
Entropy, Perception, and Relativity does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Entropy, Perception, and Relativity, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Entropy, Perception, and Relativity will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-248764