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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1591–1600 of 2050 papers

TitleStatusHype
On the Necessity of Collaboration for Online Model Selection with Decentralized Data—0
On the overestimation of widely applicable Bayesian information criterion—0
On the relative performance of some parametric and nonparametric estimators of option prices—0
On the Reliability of Clustering Stability in the Large Sample Regime—0
On the Role of Supervision in Unsupervised Constituency Parsing—0
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data—0
On the safe use of prior densities for Bayesian model selection—0
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation—0
On the Uncomputability of Partition Functions in Energy-Based Sequence Models—0
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms—0
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