SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1471–1480 of 2050 papers

TitleStatusHype
Minimum Description Length Revisited—0
Hybrid methodology based on Bayesian optimization and GA-PARSIMONY to search for parsimony models by combining hyperparameter optimization and feature selection—0
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models—0
Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools—0
metric-learn: Metric Learning Algorithms in PythonCode0
Convergence Rates of Variational Inference in Sparse Deep Learning—0
Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning—0
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces—0
Multi-view Deep Subspace Clustering NetworksCode0
On the Existence of Simpler Machine Learning Models—0
Show:102550
← PrevPage 148 of 205Next →

No leaderboard results yet.