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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 851–875 of 2050 papers

TitleStatusHype
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—0
Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage—0
Graph-based regularization for regression problems with alignment and highly-correlated designs—0
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models—0
Graphical LASSO Based Model Selection for Time Series—0
Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor—0
Graph Similarity Description: How Are These Graphs Similar?—0
Graph vs. Sequence: An Empirical Study on Knowledge Forms for Knowledge-Grounded Dialogue—0
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks—0
Greedy equivalence search for nonparametric graphical models—0
Greedy metrics in orthogonal greedy learning—0
Greedy Model Averaging—0
Fine-Tuning Video Transformers for Word-Level Bangla Sign Language: A Comparative Analysis for Classification Tasks—0
Green Runner: A tool for efficient deep learning component selection—0
Green Runner: A tool for efficient model selection from model repositories—0
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity—0
Find the dimension that counts: Fast dimension estimation and Krylov PCA—0
Arrival Time Prediction for Autonomous Shuttle Services in the Real World: Evidence from Five Cities—0
Agreement-based Learning—0
Guided Sampling-based Evolutionary Deep Neural Network for Intelligent Fault Diagnosis—0
FIB: A Method for Evaluation of Feature Impact Balance in Multi-Dimensional Data—0
Few-shot Adaptation of Multi-modal Foundation Models: A Survey—0
Handling Symbolic Language in Student Texts: A Comparative Study of NLP Embedding Models—0
Has the Creativity of Large-Language Models peaked? An analysis of inter- and intra-LLM variability—0
Causal Q-Aggregation for CATE Model Selection—0
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