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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 676–700 of 2050 papers

TitleStatusHype
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach—0
Cost-efficient Knowledge-based Question Answering with Large Language Models—0
Cost-Effective Online Contextual Model Selection—0
Correcting Model Bias with Sparse Implicit Processes—0
AutoEn: An AutoML method based on ensembles of predefined Machine Learning pipelines for supervised Traffic Forecasting—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Corpus-Based Paraphrase Detection Experiments and Review—0
Boosting with copula-based components—0
Convex Techniques for Model Selection—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
A model selection approach for clustering a multinomial sequence with non-negative factorization—0
Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process—0
Absolute convergence and error thresholds in non-active adaptive sampling—0
Nonlinear Causal Discovery for Grouped Data—0
Convergence Properties of Kronecker Graphical Lasso Algorithms—0
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation—0
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves—0
Continuous Bayesian Model Selection for Multivariate Causal Discovery—0
A Unified Framework for Tuning Hyperparameters in Clustering Problems—0
A ModelOps-based Framework for Intelligent Medical Knowledge Extraction—0
Continual Learning Without Knowing Task Identities: Rethinking Occam's Razor—0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing—0
A Unified Dynamic Approach to Sparse Model Selection—0
Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks—0
A Unified Approach to Routing and Cascading for LLMs—0
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