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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 1401–1425 of 2050 papers

TitleStatusHype
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Debiased Machine Learning of Set-Identified Linear Models—0
Machine-learning Growth at Risk—0
Machine learning with incomplete datasets using multi-objective optimization models—0
Mallows-type model averaging: Non-asymptotic analysis and all-subset combination—0
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks—0
Marginal likelihood and model selection for Gaussian latent tree and forest models—0
Marginal likelihood computation for model selection and hypothesis testing: an extensive review—0
Marked point processes and intensity ratios for limit order book modeling—0
Markov models for ocular fixation locations in the presence and absence of colour—0
Markov Network Structure Learning via Ensemble-of-Forests Models—0
Markov Switching Model for Driver Behavior Prediction: Use cases on Smartphones—0
Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against Myeloproliferative Neoplasms—0
Exploring the Maze of Multilingual Modeling—0
MCUBench: A Benchmark of Tiny Object Detectors on MCUs—0
Measures of Information Reflect Memorization Patterns—0
Measuring Domain Shifts using Deep Learning Remote Photoplethysmography Model Similarity—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Median Selection Subset Aggregation for Parallel Inference—0
MEDIC: Towards a Comprehensive Framework for Evaluating LLMs in Clinical Applications—0
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models—0
MESS+: Energy-Optimal Inferencing in Language Model Zoos with Service Level Guarantees—0
ME-Switch: A Memory-Efficient Expert Switching Framework for Large Language Models—0
Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games—0
Meta Learning for High-dimensional Ising Model Selection Using _1-regularized Logistic Regression—0
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