Introduction to Classification: Likelihoods, Margins, Features, and Kernels

@inproceedings{Klein2007IntroductionTC,
  title={Introduction to Classification: Likelihoods, Margins, Features, and Kernels},
  author={Dan Klein},
  booktitle={HLT-NAACL},
  year={2007}
}
Statistical methods in NLP have exploited a variety of classification techniques as core building blocks for complex models and pipelines. In this tutorial, we will survey the basic techniques behind classification. We first consider the basic principles, including the principles of maximum likelihood and maximum margin. We then discuss several core classification technologies: naive Bayes, perceptrons, logistic regression, and support vector machines. The discussion will include the key… CONTINUE READING
2 Citations
9 References
Similar Papers

Similar Papers

Loading similar papers…