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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 1161–1170 of 2050 papers

TitleStatusHype
CNN Model & Tuning for Global Road Damage DetectionCode1
Deep Time Series Models for Scarce Data—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Reframing Neural Networks: Deep Structure in Overcomplete Representations—0
Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies—0
Model Complexity of Deep Learning: A Survey—0
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases—0
General Bayesian time-varying parameter VARs for predicting government bond yields—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Partially Hidden Markov Chain Linear Autoregressive model: inference and forecastingCode0
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