Advertisement

A Regularized Linear Classifier for Effective Text Classification

  • Sharad Nandanwar
  • M. Narasimha Murty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

Abstract

In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Keywords

Support Vector Machine Naïve Bayes Classifier Regularization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery (1998)Google Scholar
  2. 2.
    Vapnik, V., Chervonenkis, A.: About Structural Risk Minimization principle. Automation Remote Control (1974)Google Scholar
  3. 3.
    Manning, C., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)zbMATHCrossRefGoogle Scholar
  4. 4.
    Murphy, K.: Naive Bayes Classifiers (2006)Google Scholar
  5. 5.
    Zipf, G.: Human Behavior and the Principle of Least Effort (1949)Google Scholar
  6. 6.
    Lasserre, J., Bishop, C., Minka, T.: Principled Hybrids of Generative and Discriminative Models. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE (2006)Google Scholar
  7. 7.
    Thomas, T., Flach, P.: WBCsvm: Weighted Bayesian Classification based on Support Vector Machines. In: Proceedings of the Eighteenth International Conference on Machine Learning. Citeseer (2001)Google Scholar
  8. 8.
    Raina, R., Shen, Y., Ng, A., McCallum, A.: Classification with Hybrid Generative/Discriminative Models. In: Advances in Neural Information Processing Systems (2003)Google Scholar
  9. 9.
    Zhang, J., Jin, R., Yang, Y., Hauptmann, A.: Modified Logistic Regression: An approximation to SVM and Its Applications in Large-scale Text Categorization. In: International Conference on Machine Learning (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sharad Nandanwar
    • 1
  • M. Narasimha Murty
    • 1
  1. 1.Department of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

Personalised recommendations