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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 1511–1520 of 2050 papers

TitleStatusHype
Multi-Objective Model Selection for Time Series Forecasting—0
A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning—0
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One—0
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining—0
Optimizing accuracy and diversity: a multi-task approach to forecast combinations—0
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language—0
Multi-View Independent Component Analysis with Shared and Individual Sources—0
Music Genre Classification: A Comparative Analysis of CNN and XGBoost Approaches with Mel-frequency cepstral coefficients and Mel Spectrograms—0
Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education—0
Navigating Uncertainty in Medical Image Segmentation—0
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