Dedication |
Foreword |
Preface |
Notation |
Introduction / 1: |
Similarity and case-based reasoning / 1.1: |
Objective of this book / 1.2: |
Making case-based inference more reliable / 1.2.1: |
The important role of models / 1.2.2: |
Formal models of case-based inference / 1.2.3: |
Overview / 1.3: |
Similarity and Case-Based Inference / 2: |
Model-based and instance-based approaches / 2.1: |
Model-based approaches / 2.1.1: |
Instance-based approaches / 2.1.2: |
Knowledge representation / 2.1.3: |
Performance in generalization / 2.1.4: |
Computational complexity / 2.1.5: |
Similarity-based methods / 2.2: |
Nearest neighbor (NN) estimation / 2.2.1: |
Instance-based learning / 2.2.2: |
Case-based reasoning / 2.2.3: |
The concept of similarity / 2.3: |
Similarity in case-based reasoning / 2.3.1: |
Similarity and fuzzy sets / 2.3.2: |
Aggregation of local similarity measures / 2.3.3: |
Case-based inference / 2.4: |
Deterministic inference problems / 2.4.1: |
Non-deterministic inference problems / 2.4.2: |
Summary and remarks / 2.4.3: |
Constraint-Based Modeling of Case-Based Inference / 3: |
Basic concepts / 3.1: |
Similarity profiles and hypotheses / 3.1.1: |
Generalized similarity profiles / 3.1.2: |
Constraint-based inference / 3.2: |
A constraint-based inference scheme / 3.2.1: |
Non-deterministic problems / 3.2.2: |
Case-based approximation / 3.3: |
Properties of case-based approximation / 3.3.1: |
Local similarity profiles / 3.3.2: |
Learning similarity hypotheses / 3.4: |
The learning task / 3.4.1: |
A learning algorithm / 3.4.2: |
Properties of case-based learning / 3.4.3: |
Experimental results / 3.4.4: |
Application to statistical inference / 3.5: |
Case-based parameter estimation / 3.5.1: |
Case-based prior elicitation / 3.5.2: |
Probabilistic Modeling of Case-Based Inference / 3.6: |
Basic probabilistic concepts / 4.1: |
Probabilistic similarity profiles and hypotheses / 4.1.1: |
Generalized probabilistic profiles / 4.1.2: |
Case-based inference, probabilistic reasoning, and statistical inference / 4.2: |
Learning probabilistic similarity hypotheses / 4.3: |
Simple hypotheses and credible case-based inference / 4.3.1: |
Extended case-based learning / 4.3.2: |
Experiments with regression and label ranking / 4.4: |
Regression: artificial data / 4.4.1: |
Regression: real-world data / 4.4.2: |
Label ranking / 4.4.3: |
Case-based inference as evidential reasoning / 4.5: |
Transformation of probabilistic evidence / 4.5.1: |
Inference from individual cases / 4.5.2: |
Combining evidence from several cases / 4.5.3: |
Assessment of cases / 4.6: |
Similarity-weighted approximation / 4.6.1: |
More general criteria / 4.6.2: |
Assessment of individual cases / 4.6.3: |
Complex similarity hypotheses / 4.7: |
Inference schemes of higher order / 4.7.1: |
Partially admissible profiles / 4.7.2: |
Approximate probabilistic inference / 4.8: |
Generalized uncertainty measures and profiles / 4.8.1: |
An approximate inference scheme / 4.8.2: |
Fuzzy Set-Based Modeling of Case-Based Inference I / 4.9: |
Background on possibility theory / 5.1: |
Possibility distributions as generalized constraints / 5.1.1: |
Possibility as evidential support / 5.1.2: |
Fuzzy rule-based modeling of the CBI hypothesis / 5.2: |
Possibility rules / 5.2.1: |
Modeling the CBI hypothesis / 5.2.2: |
Generalized possibilistic prediction / 5.3: |
Control of compensation and accumulation of support / 5.3.1: |
Possibilistic support and weighted NN estimation / 5.3.2: |
Upper and lower possibility bounds / 5.3.3: |
Fuzzy logical evaluation / 5.3.4: |
Comparison of extrapolation principles / 5.3.5: |
From predictions to decisions / 5.3.6: |
An illustrative example / 5.3.7: |
Complexity issues / 5.3.8: |
Extensions of the basic model / 5.4: |
Dealing with incomplete information / 5.4.1: |
Discounting noisy and atypical instances / 5.4.2: |
From instances to rules / 5.4.3: |
Modified possibility rules / 5.4.4: |
Combination of several rules / 5.4.5: |
Locally restricted extrapolation / 5.4.6: |
Incorporation of background knowledge / 5.4.7: |
Experimental studies / 5.5: |
Preliminaries / 5.5.1: |
Classification accuracy / 5.5.2: |
Statistical assumptions and robustness / 5.5.3: |
Variation of the aggregation operator / 5.5.4: |
Representation of uncertainty / 5.5.5: |
Calibration of CBI models / 5.6: |
Relations to other fields / 5.7: |
Fuzzy and possibilistic data analysis / 5.7.1: |
Fuzzy set-based approximate reasoning / 5.7.2: |
Fuzzy Set-Based Modeling of Case-Based Inference II / 5.8: |
Gradual inference rules / 6.1: |
The basic model / 6.1.1: |
Modification of gradual rules / 6.1.2: |
Certainty rules / 6.2: |
Cases as information sources / 6.3: |
A probabilistic model / 6.3.1: |
Combination of information sources / 6.3.2: |
Exceptionality and assessment of cases / 6.4: |
Local rules / 6.5: |
Case-Based Decision Making / 6.6: |
Case-based decision theory / 7.1: |
Nearest Neighbor decisions / 7.2: |
Nearest Neighbor classification and decision making / 7.2.1: |
Nearest Neighbor decision rules / 7.2.2: |
An axiomatic characterization / 7.2.3: |
Fuzzy modeling of case-based decisions / 7.3: |
Basic measures for act evaluation / 7.3.1: |
Modification of the basic measures / 7.3.2: |
Interpretation of the decision criteria / 7.3.3: |
Fuzzy quantification in act evaluation / 7.4: |
A CBI framework of CBDM / 7.5: |
Generalized decision-theoretic setups / 7.5.1: |
Decision making using belief functions / 7.5.2: |
Possibilistic decision making / 7.5.3: |
CBDM models: A discussion of selected issues / 7.6: |
The relation between similarity, preference, and belief / 7.6.1: |
The effect of observed cases / 7.6.2: |
Dynamic aspects of decision making / 7.6.3: |
Experience-based decision making / 7.7: |
Compiled decision models / 7.7.1: |
Satisficing decision trees / 7.7.2: |
Experimental evaluation / 7.7.3: |
Conclusions and Outlook / 7.8: |
Possibilistic Dominance in Qualitative Decisions / A: |
Implication-Based Fuzzy Rules as Randomized Gradual Rules / B: |
Implication-based fuzzy rules / B.1: |
Gradual rules / B.1.1: |
Other implication-based rules / B.1.2: |
Randomized gradual rules / B.2: |
A probabilistic representation of implication-based fuzzy rules / B.3: |
Similarity-Based Reasoning as Logical Inference / C: |
Simulation Results of Section 3.4.4 / D: |
Experimental Results of Section 5.5.4 / E: |
Simulation Results of Section 7.4 / F: |
Computation of an Extended Splitting Measures / G: |
Experimental Results of Section 7.7.2 / H: |
References |
Index |