Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification

Miller and Yan. "Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification." Neural Computation, 2000. doi:10.1162/089976600300015105

[Miller and Yan. "Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification." Neural Computation, 2000.](https://mlanthology.org/neco/2000/miller2000neco-approximate/) doi:10.1162/089976600300015105

@article{miller2000neco-approximate,
  title     = {{Approximate Maximum Entropy Joint Feature Inference Consistent with Arbitrary Lower-Order Probability Constraints: Application to Statistical Classification}},
  author    = {Miller, David J. and Yan, Lian},
  journal   = {Neural Computation},
  year      = {2000},
  pages     = {2175-2207},
  doi       = {10.1162/089976600300015105},
  volume    = {12},
  url       = {https://mlanthology.org/neco/2000/miller2000neco-approximate/}
}