Authors:
Maria Semenkina
;
Shakhnaz Akhmedova
and
Eugene Semenkin
Affiliation:
Siberian State Aerospace University, Russian Federation
Keyword(s):
Nonlinguistic Information Extraction, Semi-supervised Learning, Bio-inspired Algorithms, Evolutionary Algorithms.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Soft Computing
Abstract:
The concept of nonlinguistic information includes all types of extra linguistic information such as factors of
age, emotion and physical states, accent and others. Semi-supervised techniques based on using both
labelled and unlabelled examples can be an efficient tool for solving nonlinguistic information extraction
problems with large amounts of unlabelled data. In this paper a new cooperation of biology related
algorithms (COBRA) for semi-supervised support vector machines (SVM) training and a new self-configuring
genetic algorithm (SelfCGA) for the automated design of semi-supervised artificial neural
networks (ANN) are presented. Firstly, the performance and behaviour of the proposed semi-supervised
SVMs and semi-supervised ANNs were studied under common experimental settings; and their workability
was established. Then their efficiency was estimated on a speech-based emotion recognition problem.