Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Text classification with kernels on the multinomial manifold
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A dimensionality reduction algorithm and its application for interactive visualization
Journal of Visual Languages and Computing
Linear feature-based models for information retrieval
Information Retrieval
Active Learning by Spherical Subdivision
The Journal of Machine Learning Research
Geometric Characterizations of Graphs Using Heat Kernel Embeddings
Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII
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The assumptions behind linear classifiers for categorical data are examined and reformulated in the context of the multinomial manifold, the simplex of multinomial models furnished with the Riemannian structure induced by the Fisher information. This leads to a new view of hyperplane classifiers which, together with a generalized margin concept, shows how to adapt existing margin-based hyperplane models to multinomial geometry. Experiments show the new classification framework to be effective for text classification, where the categorical structure of the data is modeled naturally within the multinomial family.