The Third Workshop on Synergies between
CBR and Machine Learning
will be held at the
International Conference on Case-Based Reasoning (ICCBR-18)
in Stockholm, Sweden on July 10, 2018
Call for Papers
At the core of CBR lies the ability of a system to learn from past cases. However, CBR systems often incorporate machine learning methods, for example, to organize their memory or to learn adaptation rules. In turn, machine learning systems often utilize CBR as a learning methodology, for example, through a common set of problems with the nearest-neighbor method and reinforcement learning. Meanwhile, the machine learning community, which is tightly coupled with knowledge discovery, has historically included CBR among the types of instance-based learning.
This workshop will be dedicated to studying in-depth the possible synergies between case-based reasoning (CBR) and machine learning. It also aims at identifying potentially fruitful ideas for co-operative problem-solving where both CBR and machine learning researchers can compare and combine methods. In particular, new advances in machine learning may help CBR to advance its field of study and play a vital role in its future. This third Workshop on Synergies between CBR and Machine Learning aims to:
- provide a forum for identifying important contributions and opportunities for research on combining CBR and machine learning,
- promote the systematic study of how to synergistically integrate CBR and machine learning,
- showcase synergistic systems using CBR and machine learning.
Some of the technical issues addressed, and potential outcomes of the workshop, are to identify the machine learning methods used in CBR, to categorize the problems addressed by machine learning in CBR, to propose methodological improvements to fit this context’s needs, preferred types and methods, and guidelines to better develop CBR systems taking advantage of all machine learning research has to offer. Similarly, the workshop will identify the CBR methods used in machine learning, categorize the problems addressed by CBR in machine learning, propose methodological improvements to fit this context’s needs, preferred types and methods, and guidelines to better develop machine learning systems taking advantage of all CBR research has to offer.
We welcome all those interested in the problems and promise of synergistically combining CBR and machine learning whether they belong to the CBR community, the machine learning community, or the knowledge discovery community.
Topics of interest include (but are not limited to):
- Architectures for synergistic systems between CBR and machine learning
- Theoretical frameworks for synergistic systems between CBR and machine learning
- Memory structure mining in CBR
- Memory organization mining in CBR (decision tree induction, etc.)
- Case mining
- Feature selection in CBR
- Knowledge discovery in CBR (adaptation knowledge, meta-knowledge, etc.)
- Concept mining in CBR
- Image and multimedia mining in CBR
- Temporal mining in CBR
- Text mining in CBR
- Signal mining in CBR
- Process mining in CBR
- Web mining and CBR
- Nearest-neighbor systems and CBR
- Instance-based learning and CBR
- Reinforcement learning and CBR
- CBR and statistics
- CBR and statistical data analysis
- CBR in multi-strategy learning systems
- CBR and similarity and metric learning
- CBR and Big Data
- CBR and deep learning
- Application specific synergies between CBR and machine learning (medicine, bioinformatics, social networks, sentiment analysis, etc.).
Paper presentations will be interspersed with discussions in which we characterize, categorize, and discuss the synergies between CBR and machine learning. A wrap-up round table discussion will summarize the lessons learnt, issues identified, and future directions.
We welcome two types of papers:
- Full-length research papers are limited to 10 pages in length.
- Short research papers are limited to 4 pages in length. Short papers should present late breaking work, position statements, or work in progress.
All papers are to be submitted via the CBRML 2018 EasyChair system. Papers should be in Springer LNCS format. Author's instructions, along with LaTeX and Word macro files, are available at http://www.springer.de/comp/lncs/authors.html.
Submissions should be original papers that have not already been published elsewhere. However, papers may include previously published results that support a new theme, as long as all past publications are fully referenced.
- Submission Deadline: June 13, 2018
- Notification Date: June 25, 2018
- Camera-Ready Deadline: July 2, 2018
- Workshop Date: July 10, 2018
Workshop Web Site
Submission Web Site
State University of New York at Oswego, USA
Ohio University, USA
University of Piemonte Orientale, Italy
Agnar Aamodt, NTNU, Norway
Klaus-Dieter Althoff, DFKI and University of Hildesheim, Germany
Jean Lieber, Loria, University of Nancy, France
Luigi Portinale, University of Piemonte Orientale, Italy
Rainer Schmitt, University of Rostock, Germany
Olga Vorobieva, I. M. Sechenov Institute of Evolutionary Physiology and Biochemistry, Russia
We look forward to welcoming you to Stockholm !