Reparametrization-covariant theory for on-line learning of probability distributions
2001
: We discuss the on-line learning of probability distributions in a reparametrization covariant formulation. Reparametrization covariance plays an essential role not only to respect an intrinsic property of "information" but also for pattern recognition problems. We can obtain an optimal on-line learning algorithm with reparametrization invariance, where the conformal gauge connects a covariant formulation with a noncovariant one in a natural way.
Keywords:
- Conditional mutual information
- Probability distribution
- Convolution of probability distributions
- Classical mechanics
- Algebra of random variables
- Statistics
- Random variable
- Mathematical analysis
- Inverse Gaussian distribution
- Mathematical statistics
- Probability mass function
- Mathematics
- Mathematical physics
- Artificial intelligence
- Covariant transformation
- Pattern recognition
- Invariant (physics)
- Correction
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