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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 1321–1330 of 2050 papers

TitleStatusHype
Train on Validation: Squeezing the Data Lemon—0
Transfer Learning via Auxiliary Labels with Application to Cold-Hardiness Prediction—0
Transformers4NewsRec: A Transformer-based News Recommendation Framework—0
Bayesian Image Classification with Deep Convolutional Gaussian Processes—0
Trinary Tools for Continuously Valued Binary Classifiers—0
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection—0
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services—0
Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals—0
Tryage: Real-time, intelligent Routing of User Prompts to Large Language Models—0
Tuning for Trustworthiness -- Balancing Performance and Explanation Consistency in Neural Network Optimization—0
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