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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 1071–1080 of 2050 papers

TitleStatusHype
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies—0
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption—0
Random Models for Fuzzy Clustering Similarity Measures—0
Ranking pre-trained segmentation models for zero-shot transferability—0
Ranking & Reweighting Improves Group Distributional Robustness—0
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms—0
Rational kernel-based interpolation for complex-valued frequency response functions—0
Rational-WENO: A lightweight, physically-consistent three-point weighted essentially non-oscillatory scheme—0
Realistic Evaluation of Deep Partial-Label Learning Algorithms—0
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