International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Recursive Adaptive ECOC Models
EPIA '01 Proceedings of the10th Portuguese Conference on Artificial Intelligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems, Logic Programming and Constraint Solving
A Generalized Class of Boosting Algorithms Based on Recursive Decoding Models
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Correctness of Local Probability Propagation in Graphical Models with Loops
Neural Computation
Iterative decoding of concatenated codes: a tutorial
EURASIP Journal on Applied Signal Processing
Joint source-channel decoding of variable-length codes with soft information: a survey
EURASIP Journal on Applied Signal Processing
Identification of best sets of actions in Influence Nets
International Journal of Hybrid Intelligent Systems
Cost-sensitive learning with conditional Markov networks
Data Mining and Knowledge Discovery
Efficient decoding of turbo codes with nonbinary belief propagation
EURASIP Journal on Wireless Communications and Networking - Advances in Error Control Coding Techniques
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Belief propagation in networks of spiking neurons
Neural Computation
Simulated Iterative Classification A New Learning Procedure for Graph Labeling
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Cutset sampling for Bayesian networks
Journal of Artificial Intelligence Research
Convergence analysis of generalized serial message-passing schedules
IEEE Journal on Selected Areas in Communications - Special issue on capaciyy approaching codes
A MIMO receiver for WiMAX system
CSNA '07 Proceedings of the IASTED International Conference on Communication Systems, Networks, and Applications
A probabilistic LDPC-coded fault compensation technique for reliable nanoscale computing
IEEE Transactions on Circuits and Systems II: Express Briefs
A variational inference framework for soft-in soft-out detection in multiple-access channels
IEEE Transactions on Information Theory
Analysis of LDPC decoding schedules
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
Dynamic schedules based on variable nodes residual for LDPC codes
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Good error correcting output codes for adaptive multiclass learning
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Journal of Intelligent and Robotic Systems
A min-sum iterative decoder based on pulsewidth message encoding
IEEE Transactions on Circuits and Systems II: Express Briefs
Effective Variants of the Max-Sum Algorithm for Radar Coordination and Scheduling
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Semi-supervised probability propagation on instance-attribute graphs
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
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We present a unified graphical model framework for describing compound codes and deriving iterative decoding algorithms. After reviewing a variety of graphical models (Markov random fields, Tanner graphs, and Bayesian networks), we derive a general distributed marginalization algorithm for functions described by factor graphs. From this general algorithm, Pearl's (1986) belief propagation algorithm is easily derived as a special case. We point out that iterative decoding algorithms for various codes, including “turbo decoding” of parallel-concatenated convolutional codes, may be viewed as probability propagation in a graphical model of the code. We focus on Bayesian network descriptions of codes, which give a natural input/state/output/channel description of a code and channel, and we indicate how iterative decoders can be developed for parallel-and serially concatenated coding systems, product codes, and low-density parity-check codes