Machine Learning - The Art and Science of Algorithms that Make Sense of Data

By Peter A. Flach. 2012

967 citations recorded by the source corpus.

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Citations in the corpus

Learning Optimal Classification Trees Using a Binary Linear Program Formulation (2019, background): “We assume readers to be familiar with classiﬁcation trees, see, e.g., (Flach 2012).”
On Defining Artificial Intelligence (2019, methodology): “• Though this individual-level experience-driven adaptation is often referred to as “learning,” what my definition proposes is very different from mainstream machine learning algorithms at the present time (Flach, 2012; LeCun, Bengio, and Hinton, 2015; Domingos, 2018), where a learning process is de”
Dash: Semi-Supervised Learning with Dynamic Thresholding (2021, background): “There has been growing interest in semi-supervised learning for training machine learning and deep learning [Flach, 2012, Goodfellow et al., 2016].”
Regression diagnostics meets forecast evaluation: conditional calibration, reliability diagrams, and coefficient of determination (2021, methodology): “While the PAV algorithm has been used extensively for the recalibration of probabilistic classifiers (e.g., Flach, 2012), we are unaware of any extant work that uses Algorithm 1 for forecast recalibration, forecast evaluation, or model diagnostics in non-binary settings.”
Learning fuzzy decision trees using integer programming (2018, unknown): “More information can be found in [16].”
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers (2021, background): “The area under the curve is a common summary of trade-off curves in machine learning [13, 11, 12, 19].”
Learning Decision Trees with Flexible Constraints and Objectives Using Integer Optimization (2017, unknown): “We refer to [9] for more information.”
Explainable AI under contract and tort law: legal incentives and technical challenges (2020, background): “Flach (2012) categorizes machine learning models into seven broad classes:”
Feature selection for high-dimensional data (2016, background): “However, although visualizations are allowed for several feature extraction techniques [22,65], that is not the case for feature selection methods that are preferably used when model interpretability is necessary, as the former transform the original features into a new set of features, while the la”
Algorithmic surveillance: the collection conundrum (2017, background): “In addition, precise weightings can be applied to different factors in the profile (Flach 2012, 25 – 32).”
Machine learning for metagenomics: methods and tools (2015, background): “To design a program able to learn how to solve a given task, Flach (Flach, 2012) indicates the following requirements.”
UX Design Innovation: Challenges for Working with Machine Learning as a Design Material (2017, background): “ML is neither arcane nor obscure, with numerous textbooks [21,54], introductory articles [16] and online resources [24] covering the topic.”
The production of prediction: What does machine learning want? (2015, methodology): “…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, 2013)…”