The importance of outlier detection and training set selection for reliable environmental QSAR predictions

Chemosphere ◽  
2006 ◽  
Vol 63 (1) ◽  
pp. 99-108 ◽  
Author(s):  
Erik Furusjö ◽  
Anders Svenson ◽  
Magnus Rahmberg ◽  
Magnus Andersson
Keyword(s):  
Outlier Detection ◽  
Training Set ◽  
Selection For ◽  
Training Set Selection ◽  
And Training

scholarly journals Multilingual Training Set Selection for ASR in Under-Resourced Malian Languages

2021 ◽  
pp. 749-760
Author(s):  
Ewald van der Westhuizen ◽  
Trideba Padhi ◽  
Thomas Niesler
Keyword(s):  
Training Set ◽  
Selection For ◽  
Training Set Selection

A multi-objective evolutionary approach to training set selection for support vector machine

2018 ◽  
Vol 147 ◽  
pp. 94-108 ◽  
Author(s):  
Giovanni Acampora ◽  
Francisco Herrera ◽  
Genoveffa Tortora ◽  
Autilia Vitiello
Keyword(s):  
Support Vector Machine ◽  
Evolutionary Approach ◽  
Support Vector ◽  
Training Set ◽  
Multi Objective ◽  
Selection For ◽  
Training Set Selection

scholarly journals Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ignacio Fernández Anitzine ◽  
Juan Antonio Romo Argota ◽  
Fernado Pérez Fontán
Keyword(s):  
Path Loss ◽  
Propagation Path ◽  
Training Set ◽  
Received Power ◽  
Optimum Selection ◽  
Combined Use ◽  
Artificial Neural ◽  
Training Set Selection ◽  
And Training ◽  
Selection Of

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.

scholarly journals Training Set Selection for Building Compact and Efficient Language Models

2009 ◽  
Vol E92-D (3) ◽  
pp. 506-511
Author(s):  
Keiji YASUDA ◽  
Hirofumi YAMAMOTO ◽  
Eiichiro SUMITA
Keyword(s):  
Language Models ◽  
Training Set ◽  
Selection For ◽  
Training Set Selection
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