• Publications
  • Influence
Introduction to machine learning
  • Ethem Alpaydin
  • Computer Science
  • Adaptive computation and machine learning
  • 1 October 2004
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systemsExpand
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Multiple Kernel Learning Algorithms
In recent years, several methods have been proposed to combine multiple kernels instead of using a single one. These different kernels may correspond to using different notions of similarity or mayExpand
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Localized multiple kernel learning
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is optimized duringExpand
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Introduction to Machine Learning (Adaptive Computation and Machine Learning)
  • 338
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Combined 5 2 cv F Test for Comparing Supervised Classification Learning Algorithms
  • Ethem Alpaydin
  • Computer Science, Mathematics
  • Neural Computation
  • 1 November 1999
Dietterich (1998) reviews five statistical tests and proposes the 5 2 cvt test for determining whether there is a significant difference between the error rates of two classifiers. In ourExpand
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Multiple networks for function learning
  • Ethem Alpaydin
  • Computer Science
  • IEEE International Conference on Neural Networks
  • 28 March 1993
In the case of learning a mapping, it is proposed to build several possible models instead of one, train them all independently on the same task and take a vote over their responses. These networksExpand
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Introduction to machine learning, 2rd ed
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Introduction to Machine Learning: The MIT Press
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Multiclass Posterior Probability Support Vector Machines
Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise andExpand
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