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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 1776–1800 of 2050 papers

TitleStatusHype
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss—0
Deep Convolutional Neural Networks for Smile Recognition—0
Deep Elastic Networks with Model Selection for Multi-Task Learning—0
Deep Gaussian Processes—0
Deep learning for scene recognition from visual data: a survey—0
Deep Learning For Smile Recognition—0
Deep Learning Inversion of Electrical Resistivity Data—0
Deep Learning Models for UAV-Assisted Bridge Inspection: A YOLO Benchmark Analysis—0
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022—0
On model selection for scalable time series forecasting in transport networks—0
Deep Online Convex Optimization by Putting Forecaster to Sleep—0
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation—0
Deep Time Series Models for Scarce Data—0
Degrees of Freedom and Information Criteria for the Synthetic Control Method—0
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models—0
Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models—0
Dependence model assessment and selection with DecoupleNets—0
Deploying Offline Reinforcement Learning with Human Feedback—0
Derivative-Free Reinforcement Learning: A Review—0
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case—0
Detecting seasonal episodic-like spatiotemporal memory patterns using animal movement modelling—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Design and Scheduling of an AI-based Queueing System—0
Designing Ecosystems of Intelligence from First Principles—0
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework—0
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