Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
The Canonical Distortion Measure for Vector Quantization and Function Approximation
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
The Journal of Machine Learning Research
A generative theory of relevance
A generative theory of relevance
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Measuring the variability in effectiveness of a retrieval system
IRFC'10 Proceedings of the First international Information Retrieval Facility conference on Adbances in Multidisciplinary Retrieval
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We introduce perturbation kernels , a new class of similarity measure for information retrieval that casts word similarity in terms of multi-task learning. Perturbation kernels model uncertainty in the user's query by choosing a small number of variations in the relative weights of the query terms to build a more complete picture of the query context, which is then used to compute a form of expected distance between words. Our approach has a principled mathematical foundation, a simple analytical form, and makes few assumptions about the underlying retrieval model, making it easy to apply in a broad family of existing query expansion and model estimation algorithms.