Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Classification on pairwise proximity data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Modern Information Retrieval
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Hyperplane margin classifiers on the multinomial manifold
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Journal of Machine Learning Research
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Extracting key-substring-group features for text classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Meta-algorithmic systems for document classification
Proceedings of the 2006 ACM symposium on Document engineering
Extending the single words-based document model: a comparison of bigrams and 2-itemsets
Proceedings of the 2006 ACM symposium on Document engineering
Linear feature-based models for information retrieval
Information Retrieval
Proceedings of the 25th international conference on Machine learning
c-Means Clustering on the Multinomial Manifold
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Kernel-Based Text Classification on Statistical Manifold
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Text Classification on Embedded Manifolds
IBERAMIA '08 Proceedings of the 11th Ibero-American conference on AI: Advances in Artificial Intelligence
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Ontology-Based similarity between text documents on manifold
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Proceedings of the 21st ACM international conference on Information and knowledge management
Scene recognition on the semantic manifold
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Segmental multi-way local pooling for video recognition
Proceedings of the 21st ACM international conference on Multimedia
Multimedia event detection with multimodal feature fusion and temporal concept localization
Machine Vision and Applications
Journal of Signal Processing Systems
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Support Vector Machines (SVMs) have been very successful in text classification. However, the intrinsic geometric structure of text data has been ignored by standard kernels commonly used in SVMs. It is natural to assume that the documents are on the multinomial manifold, which is the simplex of multinomial models furnished with the Riemannian structure induced by the Fisher information metric. We prove that the Negative Geodesic Distance (NGD) on the multinomial manifold is conditionally positive definite (cpd), thus can be used as a kernel in SVMs. Experiments show the NGD kernel on the multinomial manifold to be effective for text classification, significantly outperforming standard kernels on the ambient Euclidean space.