Machine Learning, Neural and Statistical Classification

By Donald Michie, David J. Spiegelhalter, Charles C. Taylor. 2009

2,917 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/fad1bd501aa769f7701c1016f8a4d1473ca77601

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In Control and Systems Engineering

Citations in the corpus

Error-backpropagation in temporally encoded networks of spiking neurons (2000, methodology): “Compared to the best machine learning methods as reported in [34], the SpikeProp results are nearly identical to those reported for traditional BP, even though we reduced the input-space 9-fold by taking as input the average of the 3x3 pixels in the environment.”
Avoiding Pitfalls in Neural Network Research (2007, background): “The lack of transparency, thus, contributes to errors in published research work.”
Understanding the Crucial Role of Attribute Interaction in Data Mining (2001, background): “Keywords: attribute interaction, classification, constructive induction, data mining, evolutionary algorithms, inductive logic programming, rule induction, rule interestingness, Simpson’s paradox, small disjuncts”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “…the 'data-driven' literature on machine learning (e.g. (Langley et al., 1987; Weiss and Kulikowski, 1991; Anthony and Biggs, 1992; Hutchinson, 1994; Michie et al., 1994; Mitchell, 1997; Vapnik, 1998; Michalski et al., 1998; Michalewicz and Fogel, 2000)) and data mining (e.g. (Adriaans and…”
Evolutionary ensembles with negative correlation learning (2000, background): “Very competitive results have been produced by EENCL in comparison with other algorithms [29].”
Rule Extraction from Trained Support Vector Machines (2005, methodology): “HRE is tested on UCI machine learning benchmark data sets [12] and Statlog collection [ 13 ].”
Rough set algorithms in classification problem (2000, neutral): “[13], [48], [58], [60], [33], [49]), decision rules (see e.”
Riemannian Manifold Hamiltonian Monte Carlo (2009, methodology): “Experimental Results for Bayesian Logistic Regression We present results from the analysis of 5 datasets (Michie et al., 1994; Ripley, 1996), summarised in Table 1.”
Automated Machine Learning: Methods, Systems, Challenges (2019, positive): “Empirically, BOHB was shown to outperform several state-of-the-art HPO methods for tuning support vector machines, neural networks and reinforcement learning algorithms, including most methods presented in this section [33].”
High dimensional structured additive regression models: Bayesian regularization, smoothing and predictive performance (2011, methodology): “In the second example, we are analysing data on the defaults of consumer credits well known under the heading “German credit data” in the classification community (see for example Michie et al. (1994) or Holmes & Adams (2002)) and available from the data archive of the Department of Statistics at th”
Comprehensible classification models: a position paper (2014, neutral): “linearly” and create independent attributes [45], [50].”