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Keywords
(7)
Case Base Reasoning
Complex Objects
Knowledge Acquisition
Machine Learning
Problem Solving
Similarity Function
Similarity Measure
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Generation of Similarity Measures from Different Sources
Generation of Similarity Measures from Different Sources,10.1007/3-540-45517-5_23,Benno Stein,Oliver Niggemann
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Generation of Similarity Measures from Different Sources
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Citations: 2
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Benno Stein
,
Oliver Niggemann
Knowledge that quantifies the similarity between
complex objects
forms a vital part of problem- solving expertise within several knowledge-intensive tasks. This paper shows how implicit knowledge about object similarities is made explicit in the form of a similarity measure. The development of a
similarity measure
is highly domain-dependent, and we will use the domain of fluidic engineering as a complex and realistic platform to present our ideas. The evaluation of the similarity between two fluidic circuits is needed for several tasks: ( i) Design problems can be supported by retrieving an existing circuit that resembles an (incomplete) circuit description. ( ii) The problem of visualizing technical documents can be reduced to the problem of arranging similar documents with respect to their similarity. The paper in hand presents new approaches for the construction of a similarity function: Based on knowledge sources that allow for an expert-friendly knowledge acquisition,
machine learning
is used to compute an explicit
similarity function
from the acquainted knowledge.
Conference:
Industrial and Engineering Applications of Artificial Intelligence and Expert Systems - IEA/AIE
, pp. 197-206, 2001
DOI:
10.1007/3-540-45517-5_23
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Citation Context
(1)
...Stein and Niggemann [
11
] used a neural network approach to learn weights of distance functions based on training examples...
...This can be accomplished by (symmetrically) widening it to be [6,
11
]...
M. Maruf Hossain
,
et al.
Improving k-Nearest Neighbour Classification with Distance Functions B...
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Citations
(2)
Improving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics
(
Citations: 3
)
M. Maruf Hossain
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,
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(
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