General Convergence Results for Linear Discriminant Updates
Machine Learning
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A statistical property of multiagent learning based on Markov decision process
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Information geometry is a general framework of Riemannian manifolds with dual affine connections. Some manifolds (e.g. the manifold of an exponential family) have natural connections (e.g. e- and m-connections) with which the manifold is dually-flat. Conversely, a dually-flat structure can be introduced into a manifold from a potential function. This paper shows the case of quasi-additive algorithms as an example.Information theory is another important tool in machine learning. Many of its applications consider information-theoretic quantities such as the entropy and the mutual information, but few fully recognize the underlying essence of them. The asymptotic equipartition property is one of the essence in information theory.This paper gives an example of the property in a Markov decision process and shows how it is related to return maximization in reinforcement learning.