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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 1701–1725 of 2050 papers

TitleStatusHype
Clustering - What Both Theoreticians and Practitioners are Doing Wrong—0
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking—0
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure—0
Cognito: Automated Feature Engineering for Supervised Learning—0
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment—0
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data—0
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders—0
Combinatorially Generated Piecewise Activation Functions—0
Combined l_1 and greedy l_0 penalized least squares for linear model selection—0
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development—0
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data—0
Communication-efficient Distributed Sparse Linear Discriminant Analysis—0
Comparative Analysis of Data Preprocessing Methods, Feature Selection Techniques and Machine Learning Models for Improved Classification and Regression Performance on Imbalanced Genetic Data—0
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission—0
Comparative Analysis of Predicting Subsequent Steps in Hénon Map—0
Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases—0
Comparing Bayesian Models of Annotation—0
Comparison between Suitable Priors for Additive Bayesian Networks—0
Comparison of Bayesian predictive methods for model selection—0
Comparison of model selection techniques for seafloor scattering statistics—0
Competing Models—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness—0
Comprehensive Exploration of Synthetic Data Generation: A Survey—0
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing—0
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