Assignment 2
Pattern Classification
Fredrik Georgsson
The assignment
- The assignment is to choose and performe one of the
objects decsribed in the list below.
Most of the projects involves in-depth studies of chapters in the course-book, often requiring gathering of
information or presentation of existing algorithms.
- The projects are to be carried out in groups of 2 or 3 persons.
The required extent of th ework is dependent of the number of persons in the group.
All members of the group shall actively take part of all part of the project and understand everything that is presented by the group.
The normal rules regarding limited cooperation do not apply for this assignment.
- The project shall be presented orally and with a report
. Oral presentations is to be given at one of two scheduled events. The oral presentation should be about 15-20 minutes for a group of two and
20-25 minutes for a group of three. All mebmers of a group must no take part of the oral presentation,
but someone in the group must be able to present the work on one of the scheduled events.
- The material in all presentations is part of the material that can appear at the written exam.
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- Last day for handing in the written report is December 10 2005
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Proposals for projects
More information regarding the projects can be found in the chapters cited by the project. When you have decided on a project,
send a message to the teacher of the course with your choice and the name of the persons in your group. The projects are distributed on a first come basis.
- Missing data (chapter 2.10) Taken
- Bayesian networks (chapter 2.11) Taken
- The EM-algorithm (chapter 3.9)
- Hidden Markov models, at least 4 pers (chapter 3.10) Taken
- Improving KNN (chapter 4.5.5)
- Fuzzy Classifiers (chapter 4.7)
- Linear Programming (chapter 5.10)
- Support
Vector Machines (kapitel 5.11) Taken
- Higher order optimization (chapter 6.9)
- Radial basis-networks (chapter 6.10.1)
- Recurrent networks (chapter 6.10.5)Taken
- Evolutionary methods (chapter 7.5)
- Parsing of Strings (chapter 8.5) Taken
- Fuzzy K-mean clustring (chapter 10.4.4)
- On-line clustring (chapter 10.11)
- Graph theoretical methods (chapter 10.12)
- Non-linear component analysis (chapter 10.13.2)
- Independent component analysis (chapter 10.13.3)
- Self organizing feature maps (chapter 10.14.1),
Your own proposals must be confirmed by the teacher before the project begins.