mlrMBO: A modular framework for model-based optimization of expensive black-box functions
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization
(MBO), also known as Bayesian optimization, which addresses the problem of expensive …
(MBO), also known as Bayesian optimization, which addresses the problem of expensive …
A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals
This work presents an efficient hybridized approach for the classification of
electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat …
electrocardiogram (ECG) samples into crucial arrhythmia classes to detect heartbeat …
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers
For many machine learning algorithms, predictive performance is critically affected by the
hyperparameter values used to train them. However, tuning these hyperparameters can …
hyperparameter values used to train them. However, tuning these hyperparameters can …
High-dimensional reliability analysis with error-guided active-learning probabilistic support vector machine: Application to wind-reliability analysis of transmission …
C Song, A Shafieezadeh, R Xiao - Journal of Structural Engineering, 2022 - ascelibrary.org
Adaptive reliability analysis methods based on surrogate models, especially kriging, have
been successfully implemented in many problems. However, the application of kriging is …
been successfully implemented in many problems. However, the application of kriging is …
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
Block coordinate descent (BCD) methods are widely used for large-scale numerical
optimization because of their cheap iteration costs, low memory requirements, amenability to …
optimization because of their cheap iteration costs, low memory requirements, amenability to …
Optimal arrangements of hyperplanes for SVM-based multiclass classification
In this paper, we present a novel SVM-based approach to construct multiclass classifiers by
means of arrangements of hyperplanes. We propose different mixed integer (linear and non …
means of arrangements of hyperplanes. We propose different mixed integer (linear and non …
Multi-objective parameter configuration of machine learning algorithms using model-based optimization
The performance of many machine learning algorithms heavily depends on the setting of
their respective hyperparameters. Many different tuning approaches exist, from simple grid …
their respective hyperparameters. Many different tuning approaches exist, from simple grid …
Let's make block coordinate descent converge faster: faster greedy rules, message-passing, active-set complexity, and superlinear convergence
Block coordinate descent (BCD) methods are widely used for large-scale numerical
optimization because of their cheap iteration costs, low memory requirements, amenability to …
optimization because of their cheap iteration costs, low memory requirements, amenability to …
[HTML][HTML] Volcanic clouds detection applying machine learning techniques to GNSS radio occultations
Volcanic clouds detection is a challenge especially when meteorological clouds are present
in the same area. Several algorithms have been developed to detect and monitor volcanic …
in the same area. Several algorithms have been developed to detect and monitor volcanic …
[PDF][PDF] Support Vector Machines for Survival Analysis with R.
This article introduces the R package survivalsvm, implementing support vector machines
for survival analysis. Three approaches are available in the package: The regression …
for survival analysis. Three approaches are available in the package: The regression …