Making use of different frameworks of approximate reasoning and reasoning under uncertainty, notably probabilistic and fuzzy set-based techniques, this book develops formal models of the above inference principle, which is fundamental to CBR. The case-based approximate reasoning methods thus obtained especially emphasize the heuristic nature of case-based inference and aspects of uncertainty in CBR.
This book constitutes the refereed proceedings of the 8th International Conference on Case-Based Reasoning, ICCBR 2009, held in Seattle, WA, USA, in July 2009. The 17 revised full papers and 17 revised poster papers presented together with 2 invited talks were carefully reviewed and selected from 55 submissions. Covering a wide range of CBR topics of interest both to practitioners and researchers, the papers are devoted to theoretical/methodological as well as to applicative aspects of current CBR analysis.
The International Conference on Case-Based Reasoning (ICCBR) is the pree- nent international meeting on case-based reasoning (CBR). ICCBR 2003 (http://www.iccbr.org/iccbr03/)isthe?fthinthisseriesofbiennialinter- tional conferences highlighting the most signi?cant contributions to the ?eld of CBR.TheconferencetookplacefromJune23throughJune26,2003attheN- wegian University of Science and Technology in Trondheim, Norway. Previous ICCBR conferences have been held in Vancouver, Canada (2001), Seeon, G- many (1999), Providence, Rhode Island, USA (1997), and Sesimbra, Portugal (1995). Day 1 of ICCBR 2003, Industry Day, provided hands-on experiences utilizing CBR in cutting-edge knowledge-management applications (e.g., help-desks,- business, and diagnostics). Day 2 featured topical workshops on CBR in the healthsciences,theimpactoflife-cyclemodelsonCBRsystems,mixed-initiative CBR, predicting time series with cases, and providing assistance with structured vs. unstructured cases. Days 3 and 4 comprised presentations and posters on theoretical and applied CBR research and deployed CBR applications, as well as invited talks from three distinguished scholars: David Leake, Indiana University, H ́ ector Munoz-Avila, ̃ Lehigh University, and Ellen Rilo?, University of Utah. The presentations and posters covered a wide range of CBR topics of in- rest both to practitioners and researchers, including case representation, si- larity, retrieval, adaptation, case library maintenance, multi-agent collaborative systems, data mining, soft computing, recommender systems, knowledge ma- gement, legal reasoning, software reuse and music.
This book constitutes the refereed proceedings of the 5th European Workshop on Case-Based Reasonning, EWCBR 2000, held in Trento, Italy in September 2000. The 40 revised full papers presented together with two invited contributions were carefully reviewed and selected for inclusion in the book. All curves issues in case-based reasoning, ranging from foundational and theoretical aspects to advanced applications in various fields are addressed.
The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies
This book constitutes the refereed proceedings of the 28th International Conference on Case-Based Reasoning Research and Development, ICCBR 2020, held in Salamanca, Spain*, in June 2020. The 20 full papers and 2 short papers presented in this book were carefully reviewed and selected from 64 submissions. The theme of ICCBR 2020, “CBR Across Bridges” was highlighted by several activities. These papers, which are included in the proceedings, address many themes related to the theory and application of case-based reasoning and its future direction. *The conference was held virtually due to the COVID-19 pandemic.
This book constitutes the proceedings of the 29th International Conference on Case-Based Reasoning, ICCBR 2021, which took place in Salamanca, Spain, during September 13-16, 2021. The 21 papers presented in this volume were carefully reviewed and selected from 85 submissions. They deal with AI and related research focusing on comparison and integration of CBR with other AI methods such as deep learning architectures, reinforcement learning, lifelong learning, and eXplainable AI (XAI).
The papers collected in this volume were presented at the 6th European C- ference on Case-Based Reasoning (ECCBR 2002) held at The Robert Gordon University in Aberdeen, UK. This conference followed a series of very succe- ful well-established biennial European workshops held in Trento, Italy (2000), Dublin, Ireland (1998), Lausanne, Switzerland (1996), and Paris, France (1994), after the initial workshop in Kaiserslautern, Germany (1993). These meetings have a history of attracting ?rst-class European and international researchers and practitioners in the years interleaving with the biennial international co- terpart ICCBR; the 4th ICCBR Conference was held in Vancouver, Canada in 2001. Proceedings of ECCBR and ICCBR conferences are traditionally published by Springer-Verlag in their LNAI series. Case-Based Reasoning (CBR) is an AI problem-solving approach where pr- lems are solved by retrieving and reusing solutions from similar, previously solved problems, and possibly revising the retrieved solution to re?ect di?erences - tween the new and retrieved problems. Case knowledge stores the previously solved problems and is the main knowledge source of a CBR system. A main focus of CBR research is the representation, acquisition and maintenance of case knowledge. Recently other knowledge sources have been recognized as important: indexing, similarity and adaptation knowledge. Signi?cant knowledge engine- ing e?ort may be needed for these, and so the representation, acquisition and maintenance of CBR knowledge more generally have become important.
