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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국지능시스템학회 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol.13 No.3
- 발행연도
- 2013.9
- 수록면
- 186 - 199 (14page)
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초록· 키워드
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We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.
목차
Abstract
1. Introduction
2. Problem Setup and Notation
3. Previous Recursive Mixture Estimation in Fully Supervised Setups
4. Semi-Supervised Recursive Discriminative Mixture Estimation
5. Evaluation
6. Conclusion
References
참고문헌 (37)
참고문헌 신청
고송 / 2011 / A Matrix-Based Genetic Algorithm for Structure Learning of Bayesian Networks / International Journal of Fuzzy Logic and Intelligent systems 11(3):135~142
조현철 / 2007 / Online parameter estimation and convergence property of dynamic bayesian networks / International Journal of Fuzzy Logic and Intelligent systems 7(4):285~294
N. Friedman / 1997 / Bayesian network classifiers / Machine Learning 29:131~163
T. Starner / 1995 / Real-time American sign language recognition from video using hidden Markov models / in Proceedings of 1995 International Symposium on Computer Vision :265~270
A. D. Wilson / 1999 / Parametric hidden Markov models for gesture recognition / IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9):884~900
조현철 / 2007 / Online parameter estimation and convergence property of dynamic bayesian networks / International Journal of Fuzzy Logic and Intelligent systems 7(4):285~294
N. Friedman / 1997 / Bayesian network classifiers / Machine Learning 29:131~163
T. Starner / 1995 / Real-time American sign language recognition from video using hidden Markov models / in Proceedings of 1995 International Symposium on Computer Vision :265~270
A. D. Wilson / 1999 / Parametric hidden Markov models for gesture recognition / IEEE Transactions on Pattern Analysis and Machine Intelligence 21(9):884~900
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