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Machine Learning – Supervised Learning
Published in Rakesh M. Verma, David J. Marchette, Cybersecurity Analytics, 2019
Rakesh M. Verma, David J. Marchette
From the above preliminary remarks, we see that any empirical risk minimization machine learning algorithm has two basic ingredients: a loss function (sometimes also called a cost function or objective function), and an algorithm that searches for an optimum of the loss function using the training data set (optimization procedure). An optional, but fairly common ingredient, is the addition of a regularization component to the loss function, which penalizes for model complexity. Recall the principle that simpler models are to be preferred.
Internet of Things with Machine Learning-Based Smart Cardiovascular Disease Classifier for Healthcare in Secure Platform
Published in Ankan Bhattacharya, Bappadittya Roy, Samarendra Nath Sur, Saurav Mallik, Subhasis Dasgupta, Internet of Things and Data Mining for Modern Engineering and Healthcare Applications, 2023
Sima Das, Jaya Das, Subrata Modak, Kaushik Mazumdar
Training: Learning or determining good values for all the biases and the weights from labelled examples is simply called training a model. Empirical risk minimization is a process in supervised learning, which is a machine learning algorithm that constructs a model and attempts to discover a model with minimal loss by examining many examples.
A Statistical Machine Learning Framework
Published in Richard M. Golden, Statistical Machine Learning, 2020
Early discussions of the empirical risk minimization framework within the context of machine learning problems may be found in Nilsson (1965, Chapter 3) and Amari (1967). Empirical risk minimization is also the basis of Vapnik’s Statistical Learning Theory (Vapnik, 2000) which plays a central role in machine learning theory.
Prediction of wear of dental composite materials using machine learning algorithms
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Abhijeet Suryawanshi, Niranjana Behera
Training data sets S, sampled from an unknown distribution D, and labeled by a target function f are provided to a supervised learning algorithm. The algorithm's goal is to identify a predictor with regard to the unknown distribution and target function that minimizes error. A sample made available to the learning algorithm is called a training dataset. Empirical risk minimization is another name for the learning paradigm used to identify a predictor hs that minimizes the training error Ls (h) as specified in Equation (1) (ERM) (Shalev-Shwartz and Ben-David 2014).