MACHINE LEARNING An Artificial Intelligence Approach

By Ryszard S. Michalski, Tom M Mitchell, Jack Mostow, Bernard Nudel, Michael Rychener, R. Quinlan, H. Simon, D. Sleeman, Robert Stepp, Paul E. Utgoff. 2009

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Recent developments in a different ﬁeld, that of machine learning (Michalski et al., 2013; Alpaydin, 2014) and neural learning (LeCun et al., 2015), also suggest the importance of learning from large input-output data.”
Deep learning for computational biology (2016, neutral): “Machine learning methods are general-purpose approaches to learn functional relationships from data without the need to define them a priori (Hastie et al , 2005; Murphy, 2012; Michalski et al , 2013).”
Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning (2023, background): “AI’s potential to revolutionize traditional teaching and learning methods has captured the attention of educators, researchers, and policymakers worldwide (Michalski et al., 2013; Ilkka, 2018).”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “As an important enabling technology for artificial intelligence, machine learning has been successfully applied in many areas, including computer vision, medical diagnosis, search engines and speech recognition [14].”
Improving Data Quality through Deep Learning and Statistical Models (2018, background): “Machine learning is a promising subfield of computer science to approach human-level artificial intelligence (AI), and is already used to self-driving car, data mining, and natural speech recognition [3-6].”
Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment (2018, background): “23 36 While these studies demonstrate possible applications of using ML in materials design, they were mostly focused on using ML models to predict properties of materials or structures rather than designing new materials with desired properties.”
Learning by Asking Questions (2017, background): “Exploratory learning centers on settings in which an agent explores the environment to acquire supervision [37, 50]; it has been studied in the context of, among others, computer games and navigation [28, 39], multi-user games [36], inverse kinematics [5], and motion planning for humanoids [14].”
Causability and explainability of artificial intelligence in medicine (2019, methodology): “ML is a very practical field of AI with the aim to develop software that can automatically learn from previous data to gain knowledge from experience and to gradually improve it's learning behavior to make predictions based on new data (Michalski, Carbonell, & Mitchell, 1984).”
Comparing different supervised machine learning algorithms for disease prediction (2019, background): “In the supervised variant, a prediction model is developed by learning a dataset where the label is known and accordingly the outcome of unlabelled examples can be predicted [9].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, methodology): “RESEARCH interests in data-driven adaptive and intelligent methods have strongly reemerged in recent years [1, 2].”