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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/4969

Title: Machine learning in embedded systems
Authors: Swere, Erick A.R.
Keywords: Artificial intelligence
Decision trees
Real time embedded systems
Incremental machine learning
Mobile robots
Frequency tables
Issue Date: 2008
Publisher: © Erick A.R. Swere
Abstract: This thesis describes novel machine learning techniques specifically designed for use in real-time embedded systems. The techniques directly address three major requirements of such learning systems. Firstly, learning must be capable of being achieved incrementally, since many applications do not have a representative training set available at the outset. Secondly, to guarantee real-time performance, the techniques must be able to operate within a deterministic and limited time bound. Thirdly, the memory requirement must be limited and known a priori to ensure the limited memory available to hold data in embedded systems will not be exceeded. The work described here has three principal contributions. • The frequency table is a data structure specifically designed to reduce the memory requirements of incremental learning in embedded systems. The frequency table facilitates a compact representation of received data that is sufficient for decision tree generation. • The frequency table decision tree (FTDT) learning method provides classification performance similar to existing decision tree approaches, but extends these to incremental learning while substantially reducing memory usage for practical problems. • The incremental decision path (IDP) method is able to efficiently induce, from the frequency table of observations, the path through a decision tree that is necessary for the classification of a single instance. The classification performance of IDP is equivalent to that of existing decision tree algorithms, but since IDP allows the maximum number of partial decision tree nodes to be determined prior to the generation of the path, both the memory requirement and the execution time are deterministic. In this work, the viability of the techniques is demonstrated through application to realtime mobile robot navigation.
Description: This thesis is Closed Access. A dissertation thesis submitted in partial fulfilment of the requirements for the award of the degree Doctor of Philosophy (PhD), at Loughborough University
URI: https://dspace.lboro.ac.uk/2134/4969
Appears in Collections:Closed Access PhD Theses (Electronic, Electrical and Systems Engineering)

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