Case-Based Reasoning and the Statistical Challenges II

@inproceedings{Perner2013CaseBasedRA,
  title={Case-Based Reasoning and the Statistical Challenges II},
  author={Petra Perner},
  booktitle={ICMMI},
  year={2013}
}
  • P. Perner
  • Published in ICMMI 2013
  • Computer Science
Case-based reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science community. The CBR community aims to develop computer models that follow this cognitive process… 

A Methodological Approach to Model CBR-based Systems

This paper presents a straightforward methodological approach to model CBR-based applications using the concepts of abstract and concrete models and intends to facilitate the CBR modeling process and to foster CBR use in various areas outside computer science.

Integration of case-based reasoning and fuzzy approaches for real-time applications in dynamic environments: current status and future directions

This paper proposes a general fuzzy-based CBR approach for real-time applications to benefit the advantages of previous approaches and presents and summarizes new findings on the integration of fuzzy approaches with CBR.

Model Development and Incremental Learning Based on Case-Based Reasoning for Signal and Image Analysis

An overview of the CBR process and its main features: similarity, memory organization, CBR learning, and case-base maintenance is provided and based on applications, what has been achieved so far is reviewed.

Individualized situation recognition using approximate case-based reasoning

In this work, Case-Based Reasoning (CBR) approach is applied to realize individualized situation recognition for supervision of human operators and it is shown that individualized situations recognition can significantly improve the recognition accuracy.

Online learning for an individualized lane-change situation recognition system applied to driving assistance

An improved Case-Based Reasoning (CBR) approach is proposed and applied for lane-change driving situation recognition and is able to improve lane- change situations recognition performance for individual human operators.

References

SHOWING 1-10 OF 70 REFERENCES

Case-Based Reasoning and the Statistical Challenges

The CBR process and the main topics within the CBR work are reviewed, including similarity, memory organization, CBR learning, and case-base maintenance and meta-learning for parameter selection, image interpretation, incremental prototype-based classification and novelty detection and handling are described.

Emergent case-based reasoning applications

The basic principle underpinning case-based reasoning (CBR) is that new problems can be solved by reusing solutions to past problems, and it has been exploited in a diverse range of areas across the arts and sciences.

Maintenance Memories: Beyond Concepts and Techniques for Case Base Maintenance

This paper exemplifies this range of research on maintenance of CBR systems by brief characterizations of the Siam methodology, the MaMa maintenance manual, and the MaSh maintenance shell.

Memory Structures and Organization in Case-Based Reasoning

  • I. Bichindaritz
  • Computer Science
    Case-Based Reasoning on Images and Signals
  • 2008
The article proceeds through an analysis of this link between data mining and CBR, then through an historical perspective referring to the theory of the dynamic memory, and finally develops the two main types of learning related to CBR memories, namely mining for memory structures andmining for memory organization.

Case-Based Reasoning

  • S. Craw
  • Computer Science
    Encyclopedia of Machine Learning
  • 2010
CBR is a term used in the fields of cognitive science and artificial intelligence for recalling cases that are similar to a target problem in order to help solve the problem.

A Taxonomy of Similarity Mechanisms for Case-Based Reasoning

  • P. Cunningham
  • Computer Science
    IEEE Transactions on Knowledge and Data Engineering
  • 2009
This paper presents a taxonomy that organizes new similarity mechanisms and more established similarity mechanisms in a coherent framework and presents research on kernel-based learning is a rich source of novel similarity representations because of the emphasis on encoding domain knowledge in the kernel function.

Utility-Oriented Matching: A New Research Direction for Case-Based Reasoning

This paper wants to extend the traditional view of CBR to overcome its former limitations, to open new fields of research, and toopen new application areas by generalizing the classical similarity measure to a utility measure that does not depend on problem descriptions taken from experience any more.

Introduction to Case-Based Reasoning for Signals and Images

  • P. Perner
  • Computer Science
    Case-Based Reasoning on Images and Signals
  • 2008
The basics of CBR are described, what has been done so far in the field of signal-interpreting systems are reviewed, and new strategies are necessary to satisfy changing environmental conditions, user needs, and process requirements.

Using k-d Trees to Improve the Retrieval Step in Case-Based Reasoning

The basic idea of the algorithm is to use the heterogeneity of the search space for a density-based structuring and to employ this precomputed structure, a k- d tree, for efficient case retrieval according to a given similarity measure.

Introspective Learning to Build Case-Based Reasoning (CBR) Knowledge Containers

Methods to learn knowledge for the retrieval and adaptation knowledge containers exploiting the knowledge already captured in the case knowledge are proposed.
...