Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
SMEM algorithm for mixture models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Support Vector Machines Based on a Semantic Kernel for Text Categorization
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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We present a generative model for constructing continuous word representations using mixtures of probabilistic PCAs. Applied to co-occurrence data, the model performs word clustering and allows the visualization of each cluster in a reduced space. In combination with a simple document model, it permits the definition of low-dimensional Fisher scores which are used as document features. We investigate the models' potential through kernel-based methods using the corresponding Fisher kernels.