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By H. J. Mclaughlin, Anna Liljestrom, J. Lim, Dawn Meyers. 2002

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Citations in the corpus

A Strategy for an Uncompromising Incremental Learner (2017, methodology): “The Learn++ is an ensemble based approach for incremental learning [23] [20].”
Balancing Between Forgetting and Acquisition in Incremental Subpopulation Learning (2022, methodology): “Other kinds of NEB methods learn a generative model (GAN) [30,34,4,34] to generate the old images for retraining or dynamically extend the models [23,22,38,5,37].”
Consensus clustering in complex networks (2012, methodology): “An alternative greedy strategy [34], which we explore here, uses the consensus matrix, i.”
Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions (2020, methodology): “Sampling phase: Each chain performs 4000 steps, which are used to approximate the posterior distribution.”
Feature Construction Methods : A Survey (2009, methodology): “A more detailed discussion can be found in [Guyon and Elisseeff, 2003].”
How to represent crystal structures for machine learning: Towards fast prediction of electronic properties (2013, methodology): “In order to include more physical knowledge about crystals, we propose a crystal representation inspired by radial distribution functions as used in the physics of x-ray powder diffraction [26] and text mining from computer science [27,28].”
Searching for Interacting Features (2007, methodology): “Representative measures used for evaluating relevance include: distance measures [Kononenko, 1994; Robnik-Sikonja and Kononenko, 2003], information measures [Fleuret, 2004], and consistency measures [Almuallim and Dietterich, 1994], to name a few.”
A Critical Review of Machine Learning of Energy Materials (2020, background): “Besides general purpose ML tools such as scikit-learn,[186] tensorflow,[187] and Pytorch,[188] there has been an explosion of customized open-source ML software libraries for materials science.”
Efficient kNN classification algorithm for big data (2016, methodology): “Thus, in order to show the effectiveness of LC-kNN algorithm, we took the kNN as the baseline and made a comparison between kNN, LC-kNN and RC-kNN (random clustering kNN), and several real datasets from UCI [2], medical imaging datasets [6,7], and LIBSVM [3] datasets.”
The physics of living neural networks (2007, background): “Recent evidence suggests that they may also occur in the hippocampus of awake rats during rest periods after intensive exploration activity [63] (called “sharp waves”), as well as during non-REM sleep [64].”
DScribe: Library of Descriptors for Machine Learning in Materials Science (2019, methodology): “For simplicity we here restrict the machine learning model to be kernel ridge regression (KRR) as implemented in the scikitlearn package [66].”
A Unified Analysis of Stochastic Momentum Methods for Deep Learning (2018, background): “, 2016], which leverages the uniform stability of a randomized algorithm [Bousquet and Elisseeff, 2002] to bound the generalization error of multiple pass SG method.”
Visual Interpretation of Kernel‐Based Prediction Models (2011, background): “Model interpretability is often discussed in conjunction with feature importance and selection.([19]) In linear modeling, the features with highest weights are considered most important.”
Feature selection for high-dimensional data (2016, background): “[52], a learning algorithm is incremental if, for a sequence of training instances (potentially batches of instances), it satisfies the following criteria:”