Graph dynamics : learning and representation
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2006
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| Online Access: | http://hdl.handle.net/1721.1/34184 |
| _version_ | 1826197624843141120 |
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| author | Ribeiro, Andre Figueiredo |
| author2 | Deb K. Roy. |
| author_facet | Deb K. Roy. Ribeiro, Andre Figueiredo |
| author_sort | Ribeiro, Andre Figueiredo |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006. |
| first_indexed | 2024-09-23T10:50:29Z |
| format | Thesis |
| id | mit-1721.1/34184 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T10:50:29Z |
| publishDate | 2006 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/341842019-04-10T13:40:20Z Graph dynamics : learning and representation Ribeiro, Andre Figueiredo Deb K. Roy. Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences Massachusetts Institute of Technology. Dept. of Architecture. Program In Media Arts and Sciences Architecture. Program In Media Arts and Sciences Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006. Includes bibliographical references (p. 58-60). Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table - where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial relations. As a framework for representation, graphs invite us to simplify and view the world as objects of pure structure whose properties are fixed in time, while the phenomena they are supposed to model are actually often changing. A node alone cannot represent a table leg, for example, because a table leg is not one structure (it can have many different shapes, colors, or it can be seen in many different settings, lighting conditions, etc.) Theories of knowledge representation have in general concentrated on the stability of symbols - on the fact that people often use properties that remain unchanged across different contexts to represent an object (in vision, these properties are called invariants). However, on closer inspection, objects are variable as well as stable. How are we to understand such problems? How is that assembling a large collection of changing components into a system results in something that is an altogether stable collection of parts? (cont.) The work here presents one approach that we came to encompass by the phrase "graph dynamics". Roughly speaking, dynamical systems are systems with states that evolve over time according to some lawful "motion". In graph dynamics, states are graphical structures, corresponding to different hypothesis for representation, and motion is the correction or repair of an antecedent structure. The adapted structure is an end product on a path of test and repair. In this way, a graph is not an exact record of the environment but a malleable construct that is gradually tightened to fit the form it is to reproduce. In particular, we explore the concept of attractors for the graph dynamical system. In dynamical systems theory, attractor states are states into which the system settles with the passage of time, and in graph dynamics they correspond to graphical states with many repairs (states that can cope with many different contingencies). In parallel with introducing the basic mathematical framework for graph dynamics, we define a game for its control, its attractor states and a method to find the attractors. From these insights, we work out two new algorithms, one for Bayesian network discovery and one for active learning, which in combination we use to undertake the object recognition problem in computer vision. To conclude, we report competitive results in standard and custom-made object recognition datasets. by Andre Figueiredo Ribeiro. S.M. 2006-09-28T15:15:49Z 2006-09-28T15:15:49Z 2006 2006 Thesis http://hdl.handle.net/1721.1/34184 69421718 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 60 p. 3309152 bytes 3311571 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Architecture. Program In Media Arts and Sciences Ribeiro, Andre Figueiredo Graph dynamics : learning and representation |
| title | Graph dynamics : learning and representation |
| title_full | Graph dynamics : learning and representation |
| title_fullStr | Graph dynamics : learning and representation |
| title_full_unstemmed | Graph dynamics : learning and representation |
| title_short | Graph dynamics : learning and representation |
| title_sort | graph dynamics learning and representation |
| topic | Architecture. Program In Media Arts and Sciences |
| url | http://hdl.handle.net/1721.1/34184 |
| work_keys_str_mv | AT ribeiroandrefigueiredo graphdynamicslearningandrepresentation |