E
Ethem Alpaydin
Researcher at Özyeğin University
Publications - 101
Citations - 9221
Ethem Alpaydin is an academic researcher from Özyeğin University. The author has contributed to research in topics: Artificial neural network & Tree (data structure). The author has an hindex of 30, co-authored 100 publications receiving 8812 citations. Previous affiliations of Ethem Alpaydin include Boğaziçi University.
Papers
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Book
Introduction to Machine Learning
TL;DR: 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.
Journal Article
Multiple Kernel Learning Algorithms
Mehmet Gönen,Ethem Alpaydin +1 more
TL;DR: 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.
Journal ArticleDOI
Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms
TL;DR: 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.
Proceedings ArticleDOI
Localized multiple kernel learning
Mehmet Gönen,Ethem Alpaydin +1 more
TL;DR: 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.
Book ChapterDOI
Support vector machines for multi-class classification
Eddy Mayoraz,Ethem Alpaydin +1 more
TL;DR: 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.