Soft Computing in Case Based ReasoningSankar Kumar Pal, Tharam S. Dillon, Daniel S. Yeung Soft Computing in Case Based Reasoning demonstrates how various soft computing tools can be applied to design and develop methodologies and systems with case based reasoning for real-life decision-making or recognition problems. Comprising contributions from experts from all over the world, it: - Provides an introduction to CBR and soft computing, and the relevance of their integration - Evaluates the strengths and weaknesses of CBR in its current form - Presents recent developments and significant applications in domains such as data-mining, medical diagnosis, knowledge-based expert systems, banking, and forensic investigation - Addresses new information on developing intelligent systems This book will be of particular interest to graduate students and researchers in computer science, electrical engineering and information technology but it will also be of interest to researchers and practitioners in the fields of systems design, pattern recognition and data mining. |
Contents
Fuzzy Sets | 16 |
On the Notion of Similarity in Case Based Reasoning and Fuzzy Theory | 29 |
Formalizing Case Based Inference Using Fuzzy Rules | 47 |
Copyright | |
13 other sections not shown
Other editions - View all
Soft Computing in Case Based Reasoning Sankar Kumar Pal,Tharam S. Dillon,Daniel S. Yeung Limited preview - 2012 |
Soft Computing in Case Based Reasoning Sankar K Pal,Tharam S Dillon,Daniel S Yeung No preview available - 2000 |
Common terms and phrases
adaptation application approach architecture Artificial Intelligence attributes based learning based reasoning based systems Case-Based Reasoning CBR system cell certainty rule chromosome classification cluster color components connection weights connectionist considered corresponding creative crew data mining database dataset defined determine diagnosis domain knowledge Equation error evaluation example experience Expert Systems fault coverages forecasting fuzzy logic fuzzy rules fuzzy sets genetic algorithms global gradual rule heuristics hidden node hybrid system IEEE images indexing input integration k-NN knowledge based layer linguistic machine learning match memory metric mission modifier Morgan Kaufmann nearest neighbor neural network neuro-fuzzy neurons operators output parameters performance phase population possible prediction problem solver Proceedings prototype query relevant representation represented retrieval Section selected similarity measure situations soft computing solution solving space strategy structure subdomains supervised learning t-norm Table techniques tion training data update vector