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Learning Graphical Model Parameters with Approximate Marginal Inference
PrePrint
ISSN: 0162-8828
| ASCII Text | x | ||
| Justin Domke, "Learning Graphical Model Parameters with Approximate Marginal Inference," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 99, no. 1, pp. 1, , 5555. | |||
| BibTex | x | ||
| @article{ 10.1109/TPAMI.2013.31, author = {Justin Domke}, title = {Learning Graphical Model Parameters with Approximate Marginal Inference}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {99}, number = {1}, issn = {0162-8828}, year = {5555}, pages = {1}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.31}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Learning Graphical Model Parameters with Approximate Marginal Inference IS - 1 SN - 0162-8828 SP EP EPD - 1 A1 - Justin Domke, PY - 5555 KW - Vectors KW - Entropy KW - Approximation algorithms KW - Optimization KW - Function approximation KW - Markov processes KW - Vision and Scene Understanding KW - Machine learning KW - Learning KW - Artificial Intelligence KW - Computing Methodologies KW - Stochastic methods KW - Segmentation KW - Image Processing and Computer Vision VL - 99 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.31
Web Extra: View Supplemental Material(PDF)
Likelihood based-learning of graphical models faces challenges of computational-complexity and robustness to model error. This paper studies methods that fit parameters directly to maximize a measure of the accuracy of predicted marginals, taking into account both model and inference approximations at training time. Experiments on imaging problems suggest marginalization-based learning performs better than likelihood-based approximations on difficult problems where the model being fit is approximate in nature.
Index Terms:
Vectors,Entropy,Approximation algorithms,Optimization,Function approximation,Markov processes,Vision and Scene Understanding,Machine learning,Learning,Artificial Intelligence,Computing Methodologies,Stochastic methods,Segmentation,Image Processing and Computer Vision
Citation:
Justin Domke, "Learning Graphical Model Parameters with Approximate Marginal Inference," IEEE Transactions on Pattern Analysis and Machine Intelligence, 06 Feb. 2013. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.31>
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