Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
A computational model of social dynamics of musical agreement
Download
index.pdf
Date
2011
Author
Öztürel, İsmet Adnan
Metadata
Show full item record
Item Usage Stats
270
views
97
downloads
Cite This
Semiotic dynamics and computational evolutionary musicology literature investigate emergence and evolution of linguistic and musical conventions by using computational multi-agent complex adaptive system models. This thesis proposes a new computational evolutionary musicology model, by altering previous models of familiarity based musical interactions that try to capture evolution of songs as a co-evolutionary process through mate selection. The proposed modified familiarity game models a closed community of agents, where individuals of the society interact with each other just by using their musical expectations. With this novel methodology, it is found that constituent agents can form a musical agreement by agreeing on a shared bi-gram musical expectation scheme. This convergence is attained in a self-organizing fashion and throughout this process significant usage of n-gram melodic lines become observable. Furthermore, modified familiarity game dynamics are investigated and it is concluded that convergence trends are dependent on simulation parameters.
Subject Keywords
Social sciences.
,
Machine Learning.
URI
http://etd.lib.metu.edu.tr/upload/12613693/index.pdf
https://hdl.handle.net/11511/21100
Collections
Graduate School of Informatics, Thesis
Suggestions
OpenMETU
Core
A Formal Methods Approach to Pattern Recognition and Synthesis in Reaction Diffusion Networks
Bartocci, Ezio; Aydın Göl, Ebru; Haghighi, Iman; Belta, Calin (2018-03-01)
We introduce a formal framework for specifying, detecting, and generating spatial patterns in reaction diffusion networks. Our approach is based on a novel spatial superposition logic, whose semantics is defined over the quad-tree representation of a partitioned image. We demonstrate how to use rule-based classifiers to efficiently learn spatial superposition logic formulas for several types of patterns from positive and negative examples. We implement pattern detection as a model-checking algorithm and we ...
A temporal neural network model for constructing connectionist expert system knowledge bases
Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01)
This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
A Trie-structured Bayesian Model for Unsupervised Morphological Segmentation
Kurfalı, Murathan; Ustun, Ahmet; CAN BUĞLALILAR, BURCU (2017-04-23)
In this paper, we introduce a trie-structured Bayesian model for unsupervised morphological segmentation. We adopt prior information from different sources in the model. We use neural word embeddings to discover words that are morphologically derived from each other and thereby that are semantically similar. We use letter successor variety counts obtained from tries that are built by neural word embeddings. Our results show that using different information sources such as neural word embeddings and letter s...
A linear approximation for training Recurrent Random Neural Networks
Halıcı, Uğur (1998-01-01)
In this paper, a linear approximation for Gelenbe's Learning Algorithm developed for training Recurrent Random Neural Networks (RRNN) is proposed. Gelenbe's learning algorithm uses gradient descent of a quadratic error function in which the main computational effort is for obtaining the inverse of an n-by-n matrix. In this paper, the inverse of this matrix is approximated with a linear term and the efficiency of the approximated algorithm is examined when RRNN is trained as autoassociative memory.
A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data
Karagoz, Gizem Nur; Yazıcı, Adnan; Dokeroglu, Tansel; Coşar, Ahmet (2020-06-01)
There are few studies in the literature to address the multi-objective multi-label feature selection for the classification of video data using evolutionary algorithms. Selecting the most appropriate subset of features is a significant problem while maintaining/improving the accuracy of the prediction results. This study proposes a framework of parallel multi-objective Non-dominated Sorting Genetic Algorithms (NSGA-II) for exploring a Pareto set of non-dominated solutions. The subsets of non-dominated featu...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
İ. A. Öztürel, “A computational model of social dynamics of musical agreement,” M.S. - Master of Science, Middle East Technical University, 2011.