Learning from Data: Concepts, Theory, and Methods

By Robert A. Lordo. Technometrics, 2001

1,107 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cc182ed73f8ecfb786a523d7c85955c4298a74f3

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Citations in the corpus

The positive false discovery rate: a Bayesian interpretation and the q-value (2003, methodology): “We use several of the basic facts about classification theory found in Cherkassky and Mulier (1998), for example.”
Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey (2018, background): “If the dimension of features is high and the size of the dataset is small, there will be “curse of dimensionality” occurring [30].”
Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression (2014, neutral): “In Original Paper Landslides 11 & (2014)430 this study, radial basis function was chosen as the kernel function due to its robustness as reported by researchers (e.g., Cherkassky and Mulier 2007; Kavzoglu and Colkesen 2009).”
The application of machine learning to structural health monitoring (2007, background): “The foundational material in this section has been discussed in considerably more detail in recent monographs—notably Vapnik (1995), Cherkassky & Mulier (1998) and Cristianini & Shawe-Taylor (2000); it is included here in order to make the paper a little more self-contained as the methods and…”
A Consensus Model to Detect and Manage Noncooperative Behaviors in Large-Scale Group Decision Making (2014, methodology): “Real-life decision making problems involving a large number of decision makers are becoming increasingly common, as what occurs, for instance, with new trends such as e-democracy processes and social networks.”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “Consequently, an LSSVR model provides global solutions to the error function which is an advantage over gradient based algorithms (e.g., FFBP-ANN) that suffer from difficulties posed by the presence of local minima (Bishop 1995; Cherkassky and Mulier 2007).”
Recent advances in surrogate-based optimization (2009, methodology): “We can prevent this by estimating the order m through a number of different criteria [8].”
Seizure prediction with spectral power of EEG using cost‐sensitive support vector machines (2011, methodology): “Therefore, we employ a double cross-validation method, where data are divided into a training set and a test set, and the training set is further subdivided into a learning set and a validation set (Friedman, 1994; Cherkassky & Mulier, 2007).”
Lagrangian Support Vector Machines (2001, methodology): “Support vector machines (SVMs) [29, 4, 5, 17, 15] are powerful tools for data classiﬁcation.”
Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, background): “Theoretical analysis such as statistical learning theory [22] seems to be required.’’”
Survey of clustering algorithms (2005, background): ““Basically, classiﬁcation systems are either supervised or unsupervised, depending on whether they assign new inputs to one of a ﬁnite number of discrete supervised classes or unsupervised categories, respectively [38], [60], [75].”
Fracture mode identification in cementitious materials using supervised pattern recognition of acoustic emission features (2014, background): “(6) incorporates a reformulation process [40] to eventually find the support vectors.”
An overview on subgroup discovery: foundations and applications (2011, background): “Among its features, we can find classification [31], regression [31], or temporal series [21].”
Proximal support vector machine classifiers (2001, methodology): “In Section 2 we introduce the proximal linear support vector machine, give the Linear Proximal Algorithm 2.1 and an ex- plicit expression for the leave-one-out-correctness in terms of problem data (16).”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, background): “While RL exploits the feedback reward, this feedback is less informative than in supervised learning, where the agent would be given the appropriate actions to take [106], [107] (although such information is not always available).”
Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm (2014, methodology): “Consequently, radiologists expect that CAD systems (Arimura, Magome, Yamashita, & Yamamot-o, 2009; Arimura, Tokunaga, Yamashita, & Kuwazuru, 2012; Cherkassky & Mulier, 2007) can improve their diagnostic abilities based on synergistic effects between the radiologist and the computer with medical…”