Fundamentals of statistical exponential families: with applications in statistical decision theory
Fundamentals of statistical exponential families: with applications in statistical decision theory
Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Neural Computation
Bayesian Ying-Yang machine, clustering and number of clusters
Pattern Recognition Letters - special issue on pattern recognition in practice V
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
On cross validation for model selection
Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
BYY harmony learning, structural RPCL, and topological self-organizing on mixture models
Neural Networks - New developments in self-organizing maps
Data smoothing regularization, multi-sets-learning, and problem solving strategies
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
A comparative investigation on subspace dimension determination
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Machine learning problems from optimization perspective
Journal of Global Optimization
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Temporal BYY learning for state space approach, hidden Markovmodel, and blind source separation
IEEE Transactions on Signal Processing
BYY harmony learning, independent state space, and generalized APT financial analyses
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Temporal BYY encoding, Markovian state spaces, and space dimension determination
IEEE Transactions on Neural Networks
Bayesian Ying-Yang Learning on Orthogonal Binary Factor Analysis
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
A Comparative Study on Data Smoothing Regularization for Local Factor Analysis
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Theoretical Analysis and Comparison of Several Criteria on Linear Model Dimension Reduction
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Machine learning problems from optimization perspective
Journal of Global Optimization
| Hi-index | 0.00 |
Two intelligent abilities and three inverse problems are reelaborated from a probability theory based two pathway perspective, with challenges of statistical learning and efforts towards the challenges overviewed. Then, a detailed introduction is provided on the Bayesian Ying-Yang (BYY) harmony learning. Proposed firstly in (Xu, 1995) and systematically developed in the past decade, this approach consists of a two pathway featured BYY system as a general framework for unifying a number of typical learning models, and a best Ying-Yang harmony principle as a general theory for parameter learning and model selection. The BYY harmony learning leads to not only a criterion that outperforms typical model selection criteria in a two-phase implementation, but also model selection made automatically during parameter learning for several typical learning tasks, with computing cost saved significantly. In addition to introducing the fundamentals, several typical learning approaches are also systematically compared and re-elaborated from the BYY harmony learning perspective. Moreover, a further brief is made on the features and applications of a particular family called Gaussian manifold based BYY systems.