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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 1211–1220 of 2050 papers

TitleStatusHype
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence—0
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation—0
A Powerful Subvector Anderson Rubin Test in Linear Instrumental Variables Regression with Conditional Heteroskedasticity—0
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source LayoutCode0
The Shape of Learning Curves: a ReviewCode0
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
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