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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 426–450 of 2050 papers

TitleStatusHype
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
Advanced Financial Reasoning at Scale: A Comprehensive Evaluation of Large Language Models on CFA Level III—0
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data—0
Coupled differentiation and division of embryonic stem cells inferred from clonal snapshots—0
A Latent Gaussian Mixture Model for Clustering Longitudinal 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
Cox process representation and inference for stochastic reaction-diffusion processes—0
A novel efficient Multi-view traffic-related object detection framework—0
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification—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
Asymmetrically Weighted CCA And Hierarchical Kernel Sentence Embedding For Image & Text Retrieval—0
Comparison of Bayesian predictive methods for model selection—0
Cost-Effective Online Contextual Model Selection—0
Selective machine learning of doubly robust functionals—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Local Projections Inference with High-Dimensional Covariates without Sparsity—0
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