Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
A new discriminative kernel from probabilistic models
Neural Computation
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
A Fast Dual Algorithm for Kernel Logistic Regression
Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Text segmentation with LDA-based Fisher kernel
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
An integrated data mining approach to real-time clinical monitoring and deterioration warning
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper introduces a nonlinear logistic regression model for classification. The main idea is to map the data to a feature space based on kernel density estimation. A discriminative model is then learned to optimize the feature weights as well as the bandwidth of a Nadaraya-Watson kernel density estimator. We then propose a hierarchical optimization algorithm for learning the coefficients and kernel bandwidths in an integrated way. Compared to other nonlinear models such as kernel logistic regression (KLR) and SVM, our approach is far more efficient since it solves an optimization problem with a much smaller size. Two other major advantages are that it can cope with categorical attributes in a unified fashion and naturally handle multi-class problems. Moveover, our approach inherits from logistic regression good interpretability of the model, which is important for clinical applications but not offered by KLR and SVM. Extensive results on real datasets, including a clinical prediction application currently under deployment in a major hospital, show that our approach not only achieves superior classification accuracy, but also drastically reduces the computing time as compared to other leading methods.