mlr: Machine Learning in R
The MLR package provides a generic, object-oriented, and extensible framework for
classification, regression, survival analysis and clustering for the R language. It provides a …
classification, regression, survival analysis and clustering for the R language. It provides a …
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 …
Effectiveness of random search in SVM hyper-parameter tuning
RG Mantovani, ALD Rossi… - … joint conference on …, 2015 - ieeexplore.ieee.org
Classification is one of the most common machine learning tasks. SVMs have been
frequently applied to this task. In general, the values chosen for the hyper-parameters of …
frequently applied to this task. In general, the values chosen for the hyper-parameters of …
Determination of egg storage time at room temperature using a low-cost NIR spectrometer and machine learning techniques
J Coronel-Reyes, I Ramirez-Morales… - … and Electronics in …, 2018 - Elsevier
Currently, consumers are more concerned about freshness and quality of food. Poultry egg
storage time is a freshness and quality indicator in industrial and consumer applications …
storage time is a freshness and quality indicator in industrial and consumer applications …
Negatively correlated search
Evolutionary algorithms (EAs) have been shown to be powerful tools for complex
optimization problems, which are ubiquitous in both communication and big data analytics …
optimization problems, which are ubiquitous in both communication and big data analytics …
An intelligent supplier evaluation model based on data-driven support vector regression in global supply chain
Supplier evaluation is an important issue in supply chain management. Most existing studies
rely on expert experience to evaluate supplier performance. In order to alleviate the …
rely on expert experience to evaluate supplier performance. In order to alleviate the …
MOI-MBO: multiobjective infill for parallel model-based optimization
B Bischl, S Wessing, N Bauer, K Friedrichs… - Learning and Intelligent …, 2014 - Springer
The aim of this work is to compare different approaches for parallelization in model-based
optimization. As another alternative aside from the existing methods, we propose using a …
optimization. As another alternative aside from the existing methods, we propose using a …
In-depth analysis of SVM kernel learning and its components
The performance of support vector machines in nonlinearly separable classification
problems strongly relies on the kernel function. Toward an automatic machine learning …
problems strongly relies on the kernel function. Toward an automatic machine learning …
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 …
[HTML][HTML] Application of machine learning techniques for predicting potential vehicle-to-pedestrian collisions in virtual reality scenarios
The definition of pedestrian behavior when crossing the street and facing potential collision
situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in …
situations is crucial for the design of new Autonomous Emergency Braking systems (AEB) in …