A randomized approximation of the MDL for stochastic models with hidden variables
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
On-Line Learning Fokker-Planck Machine
Neural Processing Letters
Training Algorithm with Incomplete Data for Feed-ForwardNeural Networks
Neural Processing Letters
Neural Networks in Non-Euclidean Spaces
Neural Processing Letters
A General Probabilistic Formulation for Supervised Neural Classifiers
Journal of VLSI Signal Processing Systems
Approximate inference in Boltzmann machines
Artificial Intelligence
An Information Geometric Perspective on Active Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
On the Need for a Neural Abstract Machine
Sequence Learning - Paradigms, Algorithms, and Applications
A Modular Neural Network Architecture with Approximation Capability and Its Applications
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
The em algorithm for kernel matrix completion with auxiliary data
The Journal of Machine Learning Research
A unified framework for model-based clustering
The Journal of Machine Learning Research
Divergence function, duality, and convex analysis
Neural Computation
Information geometry of U-Boost and Bregman divergence
Neural Computation
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A model for handling approximate, noisy or incomplete labeling in text classification
ICML '05 Proceedings of the 22nd international conference on Machine learning
Propagating distributions on a hypergraph by dual information regularization
ICML '05 Proceedings of the 22nd international conference on Machine learning
Model-based transductive learning of the kernel matrix
Machine Learning
Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation
International Journal of Computer Vision
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
Learning correlations using the mixture-of-subsets model
ACM Transactions on Knowledge Discovery from Data (TKDD)
A theoretical framework for multiple neural network systems
Neurocomputing
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Convolution of Gaussian manifolds in a differentially FED artificial neural network
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Bayesian decisions with differentially fed neural networks
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Differentially fed neural networks as ideal estimators in Bayesian space
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
Wavelet representation of differentially fed ANN
ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
An information geometry perspective on estimation of distribution algorithms: boundary analysis
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Dimension Reduction for Mixtures of Exponential Families
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Boltzmann Machines with Identified States
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Information Geometry and Its Applications: Convex Function and Dually Flat Manifold
Emerging Trends in Visual Computing
Item Preference Parameters from Grouped Ranking Observations
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Robust incremental growing multi-experts network
Applied Soft Computing
Conditional mixture model for correlated neuronal spikes
Neural Computation
A grouped ranking model for item preference parameter
Neural Computation
Aggregated information representation for technical analysis on stock market with csiszár divergence
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
The Journal of Machine Learning Research
An estimation of generalized bradley-terry models based on the em algorithm
Neural Computation
Csiszár’s divergences for non-negative matrix factorization: family of new algorithms
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Adaptive and competitive committee machine architecture
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Extended SMART algorithms for non-negative matrix factorization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Non-negative matrix factorization with quasi-newton optimization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
The Latent Maximum Entropy Principle
ACM Transactions on Knowledge Discovery from Data (TKDD)
Full Length Article: Information geometry of target tracking sensor networks
Information Fusion
Synergy, redundancy, and multivariate information measures: an experimentalist's perspective
Journal of Computational Neuroscience
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