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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 1501–1525 of 2050 papers

TitleStatusHype
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming—0
Moshi Moshi? A Model Selection Hijacking Adversarial Attack—0
MRScore: Evaluating Radiology Report Generation with LLM-based Reward System—0
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction—0
MSM lag time cannot be used for variational model selection—0
Multilevel classification framework for breast cancer cell selection and its integration with advanced disease models—0
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction—0
Multi-Model based Federated Learning Against Model Poisoning Attack: A Deep Learning Based Model Selection for MEC Systems—0
Selecting Diverse Models for Scientific Insight—0
Multi-model Stochastic Particle-based Variational Bayesian Inference for Multiband Delay Estimation—0
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
Near-Field Localization with Physics-Compliant Electromagnetic Model: Algorithms and Model Mismatch Analysis—0
Near Instance Optimal Model Selection for Pure Exploration Linear Bandits—0
Network cross-validation by edge sampling—0
Network Estimation by Mixing: Adaptivity and More—0
Network Model Selection for Task-Focused Attributed Network Inference—0
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