A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Generalized Bradley-Terry Models and Multi-Class Probability Estimates
The Journal of Machine Learning Research
A Bradley–Terry artificial neural network model for individual ratings in group competitions
Neural Computing and Applications
Bayesian inference for Plackett-Luce ranking models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
TrueSkill-Based pairwise coupling for multi-class classification
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Pairwise ranking aggregation in a crowdsourced setting
Proceedings of the sixth ACM international conference on Web search and data mining
Evaluating simulation software components with player rating systems
Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques
Uniform convergence, stability and learnability for ranking problems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Hi-index | 0.00 |
This paper describes a Bayesian approximation method to obtain online ranking algorithms for games with multiple teams and multiple players. Recently for Internet games large online ranking systems are much needed. We consider game models in which a k-team game is treated as several two-team games. By approximating the expectation of teams' (or players') performances, we derive simple analytic update rules. These update rules, without numerical integrations, are very easy to interpret and implement. Experiments on game data show that the accuracy of our approach is competitive with state of the art systems such as TrueSkill, but the running time as well as the code is much shorter.