Authors:
Bert Klauninger
;
Martin Unger
and
Horst Eidenberger
Affiliation:
Vienna University of Technology, Austria
Keyword(s):
Dual Process Model, Similarity Measures, Combined Similarity Measures, SVM Kernels, Predicative Measurements, Quantitative Measurements.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Computer Vision, Visualization and Computer Graphics
;
Data Engineering
;
Image Understanding
;
Information Retrieval
;
Kernel Methods
;
Multiclassifier Fusion
;
Multimedia
;
Multimedia Signal Processing
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Similarity and Distance Learning
;
Software Engineering
;
Telecommunications
;
Theory and Methods
Abstract:
Similarity measurement processes are a core part of most machine learning algorithms. Traditional approaches
focus on either taxonomic or thematic thinking. Psychological research suggests that a combination of both
is needed to model human-like similarity perception adequately. Such a combination is called a Similarity
Dual Process Model (DPM). This paper describes how to construct DPMs as a linear combination of existing
measures of similarity and distance. We use generalisation functions to convert distance into similarity. DPMs
are similar to kernel functions. Thus, they can be integrated into any machine learning algorithm that uses
kernel functions.Clearly, not all DPMs that can be formulated work equally well. Therefore we test classification
performance in a real-world task: the detection of pedestrians in images. We assume that DPMs are only
viable if they yield better classifiers than their constituting parts. In our experiments, we found DPM kernels
that matched the perfor
mance of conventional ones for our data set. Eventually, we provide a construction kit
to build such kernels to encourage further experiments in other application domains of machine learning.
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