Introduction to machine learning
By Ethem Alpaydin. 2004
579 citations recorded by the source corpus.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- David S. Wishart
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- Roman Eisner
- Brett Poulin
- Duane Szafron
- R. Greiner
- Paul Lu
- Ilya Sinayskiy
Citations in the corpus
- The production of prediction: What does machine learning want? (2015, background): “We only need to look to the many how-to books (Conway and White, 2012; Russell, 2011; Schutt and O’Neil, 2013), the proliferating textbooks (Alpaydin, 2010; Flach, 2012; Hastie et al., 2009; Mitchell, 1997), the abundant software libraries, the machine learning and data mining competitions (Dahl, 20”
- An introduction to quantum machine learning (2014, methodology): “be measured in terms of Shannon’s entropy [22].”
- What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews (2016, background): “● RF [1, 41] is an ensemble classifier that operates by constructing a number of decision trees at training time and outputting the class that is the mode of the classes generated by individual trees [3].”
- A review of machining monitoring systems based on artificial intelligence process models (2010, methodology): “In general, the benefits of applying a feature selection/extraction methodology to develop monitoring systems can be defined as follows [ 61 ]:”
- Software defect prediction using Bayesian networks (2012, unknown): “diagnostic inference (Alpaydın 2004).”
- A review of automatic selection methods for machine learning algorithms and hyper-parameter values (2016, methodology): “For the reasons listed in Section 2.3, many of those methods are not ideally suited for machine learning.”
- Visual Explanation of Evidence with Additive Classifiers (2006, methodology): “There are a variety of techniques for learning classifiers (Alpaydin 2004; Hastie, Tibshirani, and Friedman 2001) including decision trees, naïve Bayes (NB), support vector machines (SVM), logistic regression, linear discriminant analysis and artificial neural networks (ANN).”