LEADER 06827nam 2200577 a 450 001 9910974790403321 005 20251117083547.0 010 $a1-61728-814-4 035 $a(CKB)2670000000176623 035 $a(EBL)3020904 035 $a(SSID)ssj0000687716 035 $a(PQKBManifestationID)11415803 035 $a(PQKBTitleCode)TC0000687716 035 $a(PQKBWorkID)10755268 035 $a(PQKB)10275570 035 $a(MiAaPQ)EBC3020904 035 $a(Au-PeEL)EBL3020904 035 $a(CaPaEBR)ebr10681042 035 $a(OCoLC)788360710 035 $a(BIP)30216065 035 $a(EXLCZ)992670000000176623 100 $a20100624d2011 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCase-based reasoning $eprocesses, suitability and applications /$fAntonia M. Leeland, editor 205 $a1st ed. 210 $aHauppauge, N.Y. $cNova Science Publishers$dc2011 215 $a1 online resource (183 p.) 225 1 $aEngineering tools, techniques and tables 300 $aDescription based upon print version of record. 311 08$a1-61728-352-5 320 $aIncludes bibliographical references and index. 327 $aIntro -- CASE-BASED REASONING: PROCESSES, SUITABILITY AND APPLICATIONS -- CASE-BASED REASONING: PROCESSES, SUITABILITY AND APPLICATIONS -- LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA -- CONTENTS -- PREFACE -- Chapter 1 CASE-BASED REASONING INTEGRATIONS: APPROACHES AND APPLICATIONS -- ABSTRACT -- 1. INTRODUCTION -- 2. TRENDS IN INTEGRATIONS OF CBR WITH OTHER INTELLIGENT METHODS -- 3. REPRESENTATIVE SYSTEMS -- 3.1 Sequential Processing Approaches -- 3.1.1 Loosely coupled sequence -- 3.1.2 Tightly coupled sequence -- 3.2. Co-processing Approaches -- 3.2.1 Cooperation oriented -- 3.2.1.1 Explicit reasoning control -- 3.2.1.2 Implicit reasoning control -- 3.2.2 Reconciliation oriented -- 3.3 Embedded Processing -- 4. COMBINATION OF CBR WITH NEURULES -- 4.1 Syntax and Semantics -- 4.2 Indexing and Hybrid Inference -- CONCLUSIONS -- REFERENCES -- Chapter 2 APPLYING IMPROVED CASE INDEXING AND RETRIEVING USING EX-POST INFORMATION IN CORPORATE BANKRUPTCY PREDICTION -- ABSTRACT -- INTRODUCTION -- LITERATURE REVIEW -- Distance Metric -- 1. Linear distance metrics -- 2. Value difference metric -- Feature selection & -- determining number of cases -- Weighting features -- PROPOSED MODEL -- RESEARCH DATA AND EXPERIMENTS -- Step 1. Selecting Observation Firm Set -- Step 2. Categorizing Financial Dimensions -- Step 3. Identifying and Obtaining Candidate Financial Ratios -- Step 4. Selecting Final Financial Ratios -- Step 5. Calculating Efficiencies Using DEA for the Data Set -- Step 6. Determining Case Base -- Step 7. Dividing Experiment Sets -- Step 8. Calculating Feature Weights -- Step 9. Measuring Similarity -- Step 10. Updating Case Base -- RESULT AND ANALYSIS -- Experiment 1 -- Experiment 2 -- Unsupervised vs. Supervised -- CONCLUSION -- REFERENCES -- Chapter 3 CASE-BASED REASONING: HISTORY, METHODOLOGY AND DEVELOPMENT TRENDS -- ABSTRACT. 327 $aINTRODUCTION -- HISTORY OF CBR -- THE STEPS OF THE CBR PROCESS -- MAIN TYPES OF CBR METHODS -- Case-Based Reasoning -- Analogy-Based Reasoning -- Exemplar-Based Reasoning -- Instance-Based Reasoning -- Memory-Based Reasoning -- 5. TOOLS AND APPLICATIONS OF CBR -- 6. DEVELOPMENT TRENDS OF CBR METHODS AND APPLICATIONS -- CONCLUSION -- REFERENCES -- Chapter 4 A TEMPORAL CASE-BASED PROCEDURE FOR CANCELLATION FORECASTING: A CASE STUDY -- ABSTRACT -- 1. INTRODUCTION -- 2. CANCELLATION CURVES -- 2.1. Canceling Patterns before Departure -- 2.2. Cancellation Patterns at Departure -- 3. MODELS -- 3.1. Case-Based Predicting Model (CBP) -- 3.1.1. Similarity evaluation -- 3.1.2. Sample selection -- 3.1.3. Prediction generation -- 3.1.4. Parameter search -- 3.2. Regression Models -- 3.3. Pick up Models -- 4. EMPIRICAL STUDY -- 4.1. The Best Number of Selection -- 4.2. Comparison with a Naïve CBP Variant -- 4.3. Comparison with Four Benchmarks -- 4.4. Distributions of the Estimated Parameters -- CONCLUSION -- ACKNOWLEDGMENT -- REFERENCES -- Chapter 5 PROVISION OF SAFETY FOR TECHNOLOGICAL SYSTEMS WITH THE AID OF CASE-BASED REASONING -- ABSTRACT -- 1. INTRODUCTION -- 2. CONCEPTUALIZATION OF DATA AND KNOWLEDGE -- 3. THE CASE-BASED APPROACH -- 4. IMPLEMENTATION OF THE SOFTWARE -- 5. EXAMPLE OF APPLICATION OF THE SOFTWARE -- CONCLUSION -- REFERENCES -- Chapter 6 MATHEMATIZING THE CASE-BASED REASONING PROCESS -- ABSTRACT -- INTRODUCTION -- THE MARKOV MODEL -- MEASURING THE EFFECTIVENESS OF A CBR SYSTEM -- FUZZY SETS -- A FUZZY MODEL FOR THE REPRESENTATION OF A CBR SYSTEM -- AN APPLICATION OF THE FUZZY MODEL -- CONCLUSION -- REFERENCES -- Chapter 7 PROTOTYPE-BASED REASONING FOR DIAGNOSIS OF DYSMORPHIC SYNDROMES -- ABSTRACT -- 1. INTRODUCTION -- 1.1. Diagnostic Support for Dysmorphic Syndromes -- 1.2. Other Systems. 327 $a1.3. Case-Based Reasoning and Prototypicality Measures -- 2. DIAGNOSIS OF DYSMORPHIC SYNDROMES -- 2.1. Prototypicality Measures -- 2.2. Adaptation Rules -- 3. RESULTS -- 3.1. Application of Adaptation Rules -- 3.2. Application of Adaptation Rules -- 3.3. Application of Automatically Acquired Adaptation Rules -- 4. CONCLUSION -- REFERENCES -- Chapter 8 NEW APPROACH OF CASE-BASED REASONING* -- ABSTRACT -- 1. INTRODUCTION -- 2. NEGOTIATION -- 3. DESCRIPTION OF OUR APPROACH -- 3.1. The 3R Model -- Retrieve -- Reuse -- Retain -- 3.2. Real Estate Negotiation According to the 3R Model -- Case -- Case base -- 3.3. The 3R Model Cycle -- 3.3.1. Retrieve -- A) Retrieve 1: Optimal weights search -- B) Optimal weights specification -- B-1) Initialization of the weights -- B-2) Calculation of the similarity distance -- B-3) Adjustment of the weights -- B-4) Calculation of the optimal weights -- B) Retrieve 2: The search for the similar case -- A) Computation of the Similarity Distance in Relation to the Target -- B) Similar Case Retrieval -- 3.3.3. Retain -- 4. MODEL VALIDATION -- 5. CONCLUSION -- REFERENCES -- Commentary FUZZY SETS IN CASE-BASED REASONING -- INDEX. 330 $aCase-based reasoning (CBR) is the process of solving new problems based on the solutions of similar past problems. This book presents research in the field of CBR including business predication researches of corporate failure using CBR, and mathematising the Case-Based Reasoning process. 410 0$aEngineering tools, techniques and tables. 606 $aCase-based reasoning 615 0$aCase-based reasoning. 676 $a153.4/3 701 $aLeeland$b Antonia M$01866258 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910974790403321 996 $aCase-based reasoning$94473622 997 $aUNINA