• Publications
  • Influence
Introduction to machine learning
  • Ethem Alpaydin
  • Computer Science
  • Adaptive computation and machine learning
  • 1 October 2004
TLDR
Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts, and discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. Expand
Multiple Kernel Learning Algorithms
TLDR
Overall, using multiple kernels instead of a single one is useful and it is believed that combining kernels in a nonlinear or data-dependent way seems more promising than linear combination in fusing information provided by simple linear kernels, whereas linear methods are more reasonable when combining complex Gaussian kernels. Expand
Localized multiple kernel learning
TLDR
A localized multiple kernel learning (LMKL) algorithm using a gating model for selecting the appropriate kernel function locally and the kernel-based classifier are coupled and their optimization is done in a joint manner. Expand
Combined 5 2 cv F Test for Comparing Supervised Classification Learning Algorithms
  • Ethem Alpaydin
  • Mathematics, Computer Science
  • Neural Computation
  • 1 November 1999
TLDR
A variant of the 5 2 cvt test is proposed that combines multiple statistics to get a more robust test, and simulation results show that this combined version of the test has lower type I error and higher power than5 2 cv proper. Expand
Multiple networks for function learning
  • Ethem Alpaydin
  • Computer Science
  • IEEE International Conference on Neural Networks
  • 28 March 1993
TLDR
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, where the result of voting is better than the results of all of the networks that participated in the voting process. Expand
Introduction to machine learning, 2rd ed
Machine Learning: The New AI
TLDR
This book offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context and some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security. Expand
Multiclass Posterior Probability Support Vector Machines
TLDR
This work proposes a neighbor-based density estimator for the posterior probability support vector machine (PPSVM) and shows that the decrease in error by PPSVM is due to a decrease in bias. Expand
Support Vector Machines for Multi-class Classification
TLDR
The scaling problem of different SVMs is highlighted and various normalization methods are proposed to cope with this problem and their efficiencies are measured empirically. Expand
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