A hybrid generative/discriminative approach to text classification with additional information
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Semi-supervised classification with hybrid generative/discriminative methods
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Bias-Variance Tradeoff in Hybrid Generative-Discriminative Models
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap
Computational Statistics & Data Analysis
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Joint discriminative-generative modelling based on statistical tests for classification
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Image and Vision Computing
Object class detection: A survey
ACM Computing Surveys (CSUR)
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The interpretation of generative, discriminative and hybrid approaches to classification is discussed, in particular for the generative-discriminative tradeoff (GDT), a hybrid approach. The asymptotic efficiency of the GDT, relative to that of its generative or discriminative counterpart, is presented theoretically and, by using linear normal discrimination as an example, numerically. On real and simulated datasets, the classification performance of the GDT is compared with those of normal-based linear discriminant analysis (LDA) and linear logistic regression (LLR). Four arguments are made as follows. First, the GDT is a generative model integrating both discriminative and generative learning. It is therefore subject to model misspecification of the data-generating process and hindered by complex optimisation. Secondly, among the three approaches being compared, the asymptotic efficiency of the GDT is higher than that of the discriminative approach but lower than that of the generative approach, when no model misspecification occurs. Thirdly, without model misspecification, LDA performs the best; with model misspecification, LLR or the GDT with an optimal, large weight on its discriminative component may perform the best. Finally, LLR is affected by the imbalance between groups of data.