Hyperparameter Optimization

Hyperparameter Optimization is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Whether the algorithm is suitable for the data directly depends on hyperparameters, which directly influence overfitting or underfitting. Each model requires different assumptions, weights or training speeds for different types of data under the conditions of a given loss function.

Source: Data-driven model for fracturing design optimization: focus on building digital database and production forecast

Papers

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Showing 801–813 of 813 papers

Title	Date	Tasks	Status	Hype
Easy Hyperparameter Search Using Optunity	Dec 2, 2014	BIG-bench Machine LearningHyperparameter Optimization	CodeCode Available	0
Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images	Jun 1, 2014	Hyperparameter OptimizationSaliency Prediction	—Unverified	0
BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits	May 29, 2014	Bayesian OptimizationExperimental Design	CodeCode Available	0
ParamILS: An Automatic Algorithm Configuration Framework	Jan 15, 2014	Hyperparameter Optimization	—Unverified	0
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn	Jan 1, 2014	AutoMLBenchmarking	CodeCode Available	0
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms	Dec 12, 2013	Bayesian OptimizationBIG-bench Machine Learning	CodeCode Available	0
Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a "Null" Model be?	Jun 14, 2013	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified	0
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms	Aug 18, 2012	Bayesian OptimizationBIG-bench Machine Learning	CodeCode Available	0
Practical Bayesian Optimization of Machine Learning Algorithms	Jun 13, 2012	Bayesian OptimizationBIG-bench Machine Learning	CodeCode Available	0
Sequential Model-Based Optimization for General Algorithm Configuration	Jan 18, 2011	Hyperparameter Optimizationmodel	CodeCode Available	2
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning	Dec 12, 2010	Bayesian OptimizationHierarchical Reinforcement Learning	CodeCode Available	2
Conditional Neural Fields	Dec 1, 2009	Handwriting RecognitionHyperparameter Optimization	—Unverified	0
Rethinking LDA: Why Priors Matter	Dec 1, 2009	Hyperparameter OptimizationTopic Models	—Unverified	0

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