scholarly journals Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA

2019 ◽  
pp. 81-95 ◽  
Author(s):  
Lars Kotthoff ◽  
Chris Thornton ◽  
Holger H. Hoos ◽  
Frank Hutter ◽  
Kevin Leyton-Brown
Keyword(s):  
Model Selection ◽  
Hyperparameter Optimization ◽  
Automatic Model Selection

Neurodegenerative diseases categorization by applying the automatic model selection and hyperparameter optimization method

2021 ◽  
pp. 1-9
Author(s):  
Mirta Fuentes-Ramos ◽  
Eddy Sánchez-DelaCruz ◽  
Iván-Vladimir Meza-Ruiz ◽  
Cecilia-Irene Loeza-Mejía
Keyword(s):  
Model Selection ◽  
Neurodegenerative Diseases ◽  
Optimization Method ◽  
Human Gait ◽  
Spinocerebellar Ataxias ◽  
Hyperparameter Optimization ◽  
Automatic Model Selection ◽  
The World ◽  
High Degree

Neurodegenerative diseases affect a large part of the population in the world and also in Mexico, deteriorating gradually the quality of patients’ life. Therefore, it is important to diagnose them with a high degree of reliability. In order to solve it, various computational methods have been applied in the analysis of biomarkers of human gait. In this study, we propose employing the automatic model selection and hyperparameter optimization method that has not been addressed before for this problem. Our results showed highly competitive percentages of correctly classified instances when discriminating binary and multiclass sets of neurodegenerative diseases: Parkinson’s disease, Huntington’s disease, and Spinocerebellar ataxias.

Automatic Model Selection for Probabilistic PCA

2007 ◽  
pp. 127-134
Author(s):  
Ezequiel López-Rubio ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato ◽  
Domingo López-Rodríguez ◽  
María del Carmen Vargas-González
Keyword(s):  
Model Selection ◽  
Probabilistic Pca ◽  
Automatic Model Selection ◽  
Selection For

scholarly journals An Automatic Model Selection‐Based Machine Learning Framework to Estimate FORC Distributions

2020 ◽  
Vol 125 (10) ◽  
Author(s):  
D. Heslop ◽  
A. P. Roberts ◽  
H. Oda ◽  
X. Zhao ◽  
R. J. Harrison ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Learning Framework ◽  
Automatic Model Selection

scholarly journals Learning Structural Kernels for Natural Language Processing

2015 ◽  
Vol 3 ◽  
pp. 461-473 ◽  
Author(s):  
Daniel Beck ◽  
Trevor Cohn ◽  
Christian Hardmeier ◽  
Lucia Specia
Keyword(s):  
Natural Language Processing ◽  
Model Selection ◽  
Natural Language ◽  
Language Processing ◽  
Bayesian Methods ◽  
Training Data ◽  
Coarse Grained ◽  
Grid Search ◽  
Hyperparameter Optimization ◽  
Gradient Based

Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.

Automatic model selection for the optimization of SVM kernels

2005 ◽  
Vol 38 (10) ◽  
pp. 1733-1745 ◽  
Author(s):  
N.E. Ayat ◽  
M. Cheriet ◽  
C.Y. Suen
Keyword(s):  
Model Selection ◽  
Automatic Model Selection ◽  
Selection For
Sign in / Sign up

Export Citation Format

Share Document