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 1321–1330 of 2050 papers

TitleStatusHype
Hidden Markov Models Applied To Intraday Momentum Trading With Side Information—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
Assumption-lean inference for generalised linear model parametersCode1
Selecting the Number of Clusters K with a Stability Trade-off: an Internal Validation CriterionCode1
TensorFlow with user friendly Graphical Framework for object detection APICode1
Regret Balancing for Bandit and RL Model Selection—0
Multi-split Optimized Bagging Ensemble Model Selection for Multi-class Educational Data Mining—0
Virtual Reference Feedback Tuning with data-driven reference model selection—0
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach—0
Speedy Performance Estimation for Neural Architecture SearchCode0
Show:102550
← PrevPage 133 of 205Next →

No leaderboard results yet.