Cross-disciplinary perspectives on meta-learning for algorithm selection

By K. Smith‐Miles. 2009

591 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

AutoML: A Survey of the State-of-the-Art (2019, methodology): “It is based on the work of [164], published in 1976, and the unified framework of Smith-Miles [165](2009), which generalizes the metalearning concept to cross-disciplinary studies, exposing the similarities and differences between these approaches.”
Meta-Learning: A Survey (2018, neutral): “In many applications, domain-specific information can be leveraged as well (Smith-Miles, 2009; Olier et al., 2018).”
Meta-learning with neural networks and landmarking for forecasting model selection an empirical evaluation of different feature sets applied to industry data (2016, methodology): “In this context, meta-learning is a promising approach for model selection [8], [9], [10], [11] describing how to deploy knowledge from past tasks in order to select predictive models for new tasks with certain characteristics.”
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery (2017, background): “Adapted from (Rice, 1976; Smith-Miles, 2008). number of data points), and those that describe properties of the target protein (e.g. hydrophobicity).”
Meta-Learning (2009, unknown): “A general framework for this purpose, together with a survey of approaches, is in (Smith-Miles, 2008) [8].”
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2017, methodology): “simple, statistical or information theoretic properties that are extracted from the data set which describe the data set (Bardenet et al. 2013; Reif et al. 2012; Smith-Miles 2009).”
Automated Algorithm Selection: Survey and Perspectives (2018, background): “In the ﬁrst extensive survey in this area, Smith-Miles (2009) summarised developments in the meta-learning, artiﬁcial intelligence and operations research communities.”
Algorithm Selection for Combinatorial Search Problems: A Survey (2012, methodology): “…(Bhowmick et al., 2006), hybrid approaches that combine regression and classiﬁcation (Kotthoﬀ, 2012a), multinomial logistic regression (Samu-lowitz & Memisevic, 2007), self-organising maps (Smith-Miles, 2008b) and clustering (Stamatatos & Stergiou, 2009; Stergiou, 2009; Kadioglu et al., 2010).”
Beyond Manual Tuning of Hyperparameters (2015, background): “The problem of predicting the best performing model for a given dataset/task has been studied extensively as special case of algorithm selection within the wider area of metalearning [42, 60, 69].”
Evaluation of clustering algorithms for financial risk analysis using MCDM methods (2014, unknown): “mining and knowledge discovery [28,32,55,66].”
Combining meta-learning and search techniques to select parameters for support vector machines (2012, result): “The value of meta-features to predict learning performance has already been demonstrated in different previous work on meta-learning (see [20]).”
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020, methodology): “…treating the choice of model selection strategy as an algorithm selection problem allows us to apply methods from the field of algorithm selection (Smith-Miles, 2008; Kotthoff, 2014; Kerschke et al., 2019) and we can in future work reuse existing techniques besides the pairwise classification we…”