Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Journal of Machine Learning Research
Diffusion Kernels on Statistical Manifolds
The Journal of Machine Learning Research
Text classification with kernels on the multinomial manifold
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling word burstiness using the Dirichlet distribution
ICML '05 Proceedings of the 22nd international conference on Machine learning
Metric Learning for Text Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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In the text literature, a variety of useful kernel methods have been developed by many researchers. However, embedding text data into Euclidean space is the key characteristic of common kernels-based text categorization. In this paper, we focus on representation text vectors as points on Riemann manifold and use kernels to integrate discriminative and generative model. And then, we present diffuse kernel based on Dirichlet Compound Multinomial manifold (DCM manifold) which is a space about Dirichlet Compound Multinomial model combining inverse document frequency and information gain. More specifically, as demonstrated by our experimental results on various real-world text datasets, we show that the kernel based on this DCM manifold is more desirable than Euclidean space for text categorization. And our kernel method provides much better computational accuracy than some current state-of-the-art methods.