Methods For Combining Experts' Probability Assessments

By Robert A. Jacobs. 1995

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ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations (2019, background): “odel training data. We are interested in assigning an expert model to a given sample such that an improved performance is achieved. The expert matching problem is well-studied: (Jacobs et al. , 1991; Jacobs, 1995, 1997), starting from the seminal work of Jacobs et al. (1991), where the authors train”
Face recognition: a convolutional neural-network approach (1997, methodology): “These could be combined via simple methods such as a linear combination based on the performance of each network, or via a gating network and the expectation-maximization algorithm [11], [16].”
Ensemble based systems in decision making (2006, background): “Does the no-free-lunch theorem hold for combination rules as well? Many studies have been conducted to answer this question, including [18], [27], [28], [31], [36], [37], [42], [71], [72], among others.”
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries (2019, background): “network to determine which of the networks should be used for each sample. The gating function learned what training sample needed to be passed to which expert. For a more detailed review, please see Jacobs (1995, 1997). In order to avoid the gating function, in (Hinton et al. , 2015; Ahmed et al. ,”
Neural networks for classification: a survey (2000, background): “There are many different ways of combining individual classifiers [84], [192].”
Combining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems (2003, methodology): “We use the name “mixture rule” by analogy to the mixture of experts model (Jacobs et al. 1991), which combined expert opinions in an analogous way. In the forecasting literature, this rule is also known as the linear opinion pool; Jacobs (1995) provides a summary of the theory and applications of th”
Bidirectional recurrent neural networks (1997, unknown): “These merging procedures are referred to as linear opinion pooling and logarithmic opinion pooling, respectively [1], [7].”
A survey of multiple classifier systems as hybrid systems (2014, background): “Finally, another salient approach is the mixture of experts [146,147] which combines classifier outputs using so-called input dependent gating function.”
Decision templates for multiple classifier fusion: an experimental comparison (2001, background): “Furthermore, the probabilistic interpretation [5,12] does not lead very far without some assumptions, which may appear unrealistic and restrictive, e.”
Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015, methodology): “There are many ensemble techniques to improve the performance of algorithms [59–64], as well as other efforts in Refs.”
A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran (2016, methodology): “In Jacobs (1995) the researcher introduced a gating method to determine the weight of every expert.”
Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval (2007, methodology): “We use an affine transformation [18] to combine the”
Issues in Stacked Generalization (2011, methodology): “Jacobs (1995) reviews linear combination methods like that used in MLR. Previous work on stacked generalization, especially as applied to classi(cid:12)cation tasks, has been limited in several ways.”
A streaming ensemble algorithm (SEA) for large-scale classification (2001, background): “Specifically, several analyses of ensemble methods have shown that the correctness improved if the individual predictors make errors that axe independent of each other [14].”
Classifier ensembles: Select real-world applications (2008, methodology): “For classification problems, simple averaging reduces the model error (the error in addition to the Bayes error) by a factor of M for M independent, unbiased classifiers [55,109,110].”