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Erschienen in:
The Knowledge Increase Estimation Framework for Ontology Integration on the Instance Level Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Context Injection as a Tool for Measuring Context Usage in Machine Learning

verfasst von : Maciej Huk

Erschienen in: Intelligent Information and Database Systems

Verlag: Springer International Publishing

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Abstract

Machine learning (ML) methods used to train computational models are one of the most valuable elements of the modern artificial intelligence. Thus preparing tools to evaluate ML training algorithms abilities to find inside the training data information (the context) crucial to build successful models is still an important topic. Within this text we introduce a new method of quantitative estimation of effectiveness of context usage by the ML training algorithms based on injection of predefined context to the training data sets. The results indicate that the proposed solution can be used as a general method of analyzing differences in context processing between ML training methods.

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Vorheriges Kapitel Evaluation of Speech Perturbation Features for Measuring Authenticity in Stress Expressions
Nächstes Kapitel An Approach for Multi-Relational Data Context in Recommender Systems
Fußnoten
1
Gradient Boosting Machine training parameters: 50 trees, maximum tree depth = 5, learning rate = 0.1, implementation: H2O Flow 3.10.0.8.
 
2
Deep Neural Network training parameters: 100 × 100 hidden neurons, activation function: rectifier, max number of training epochs = 300, implementation: H2O Flow 3.10.0.8.
 
3
Random Forest classifier training parameters: bag size = 100, number of iterations = 100, unlimited tree size, implementation: Weka 3.8.0.
 
4
C4.5 tree (not pruned) implementation: C4.5 v8 by R. Quinlan.
 
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Metadaten
Titel
Context Injection as a Tool for Measuring Context Usage in Machine Learning
verfasst von
Maciej Huk
Copyright-Jahr
2017
Buch
Intelligent Information and Database Systems

Print ISBN: 978-3-319-54471-7

Electronic ISBN: 978-3-319-54472-4

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-54472-4_65

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