Databases Reference
In-Depth Information
Chapter 4
Hierarchical Model for Pattern
Recognition
The computational complexity of neural network algorithms is an important
factor in determining the effectiveness and e
ciency of a pattern recognition
scheme. The computational resource requirements, such as processing time
and memory space, are heavily impacted by increases in the computational
complexity. Therefore, an increase in the size and/or the dimensionality of the
patterns disproportionately affects the computational resource requirement.
As mentioned in Chapter 1, size and dimensionality are two key aspects in
Internet-scale pattern recognition. Internet-scale pattern recognition can be
defined as the recognition process for large-scale data. It has been influenced
by the development of sophisticated data-harvesting techniques and growth
in data storage technologies.
In Chapter 2, the theoretical background of the distributed pattern recog-
nition (DPR) scheme and some examples of DPR implementations were pre-
sented. A one-shot learning mechanism is considered important in the design
of effective and scalable DPR schemes. In Chapter 3, we presented the Graph
Neuron (GN) algorithm, a DPR scheme that uses one-shot learning. This fast
learning approach distributes learning using the adjacency comparison ap-
proach. A discussion of the limitations of the GN algorithm, including false
recalls generated by the crosstalk problem, was also presented.
In this chapter, the discussion of a GN-based DPR scheme will be extended.
This chapter will elaborate on the details of the hierarchical concept and model
for a GN implementation. The hierarchical approach eliminates the crosstalk
problem of the single-layer GN scheme. The effects of a hierarchical structure
on the complexity and scalability of the DPR scheme will also be discussed.
4.1 Evolution of One-Shot Learning: The Hierarchical
Approach
To solve the crosstalk problem in the GN pattern recognition algorithm,
Nasution and Khan [3] proposed a hierarchical structure for GN, known as the
Hierarchical Graph Neuron (HGN). The guiding principle for the development
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