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1 | (10) |
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1 | (2) |
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3 | (6) |
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9 | (2) |
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11 | (40) |
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11 | (1) |
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Type-1 Fuzzy Sets and Fuzzy Logic |
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12 | (6) |
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Characteristics of Fuzzy Sets |
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13 | (1) |
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14 | (4) |
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18 | (4) |
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Structure of Classical Logic Theory |
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18 | (1) |
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Relation of Set and Logic Theory |
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19 | (1) |
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19 | (2) |
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21 | (1) |
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22 | (6) |
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Operations on Fuzzy Relations |
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25 | (1) |
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25 | (3) |
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28 | (5) |
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29 | (2) |
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Interval Valued Type-2 Fuzzy Sets |
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31 | (1) |
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Type-2 Fuzzy Set Operations |
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32 | (1) |
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33 | (3) |
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36 | (4) |
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Extensions of Takagi-Sugeno Fuzzy Inference Systems |
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40 | (10) |
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Adaptive-Network-Based Fuzzy Inference System (ANFIS) |
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41 | (3) |
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Dynamically Evolving Neuro-Fuzzy Inference Method (DENFIS) |
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44 | (2) |
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Genetic Fuzzy Systems (GFS) |
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46 | (4) |
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50 | (1) |
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Improved Fuzzy Clustering |
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51 | (54) |
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51 | (1) |
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Fuzzy Clustering Algorithms |
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52 | (12) |
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Fuzzy C-Means Clustering Algorithm |
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53 | (5) |
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Classification of Objective Based Fuzzy Clustering Algorithms |
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58 | (1) |
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Fuzzy C-Regression Model (FCRM) Clustering Algorithm |
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58 | (3) |
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Variations of Combined Fuzzy Clustering Algorithms |
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61 | (3) |
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Improved Fuzzy Clustering Algorithm (IFC) |
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64 | (21) |
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64 | (5) |
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Improved Fuzzy Clustering Algorithm for Regression Models (IFC) |
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69 | (4) |
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Improved Fuzzy Clustering Algorithm for Classification Models (IFC-C) |
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73 | (4) |
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Justification of Membership Values of the IFC Algorithm |
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77 | (8) |
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Two New Cluster Validity Indices for IFC and IFC-C |
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85 | (18) |
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Overview of Well-Known Cluster Validity Indices |
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86 | (4) |
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The New Cluster Validity Indices |
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90 | (4) |
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Simulation Experiments [ Celikyilmaz and Turksen, 2007i;2008c] |
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94 | (6) |
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Discussions on Performances of New Cluster Validity Indices Using Simulation Experiments |
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100 | (3) |
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103 | (2) |
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105 | (44) |
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105 | (2) |
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107 | (5) |
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Proposed Type-1 Fuzzy Functions Approach Using FCM - T1FF |
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112 | (13) |
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Structure Identification of FF for Regression Models (T1FF) |
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112 | (7) |
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Structure Identification of the Fuzzy Functions for Classification Models (T1FF-C) |
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119 | (2) |
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Inference Mechanism of T1FF for Regression Models |
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121 | (1) |
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Inference Mechanism of T1FF for Classification Models |
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122 | (3) |
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Proposed Type-1 Improved Fuzzy Functions with IFC - T1IFF |
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125 | (11) |
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Structure Identification of T1IFF for Regression Models |
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125 | (6) |
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Structure Identification of T1IFF-C for Classification Models |
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131 | (1) |
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Inference Mechanism of T1IFF for Regression Problems |
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132 | (3) |
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Inference with T1IFF-C for Classification Problems |
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135 | (1) |
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Proposed Evolutionary Type-1 Improved Fuzzy Function Systems |
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136 | (11) |
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Genetic Learning Process: Genetic Tuning of Improved Membership Functions and Improved Fuzzy Functions |
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139 | (6) |
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Inference Method for ET1IFF and ET1IFF-C |
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145 | (1) |
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Reduction of Structure Identification Steps of T1IFF Using the Proposed ET1IFF Method |
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146 | (1) |
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147 | (2) |
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Modeling Uncertainty with Improved Fuzzy Functions |
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149 | (68) |
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149 | (5) |
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154 | (3) |
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Conventional Type-2 Fuzzy Systems |
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157 | (10) |
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Generalized Type-2 Fuzzy Rule Bases Systems (GT2FRB) |
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157 | (3) |
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Interval Valued Type-2 Fuzzy Rule Bases Systems (IT2FRB) |
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160 | (2) |
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Most Common Type-Reduction Methods |
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162 | (2) |
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Discrete Interval Type-2 Fuzzy Rule Bases (DIT2FRB) |
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164 | (3) |
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Discrete Interval Type-2 Improved Fuzzy Functions |
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167 | (26) |
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Background of Type-2 Improved Fuzzy Functions Approaches |
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168 | (11) |
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Discrete Interval Type-2 Improved Fuzzy Functions System (DIT2IFF) |
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179 | (14) |
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The Advantages of Uncertainty Modeling |
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193 | (3) |
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Discrete Interval Type-2 Improved Fuzzy Functions with Evolutionary Algrithms |
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196 | (19) |
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196 | (1) |
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Architecture of the Evolutionary Type-2 Improved Fuzzy Functions |
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197 | (16) |
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Reduction of Structure Identification Steps of DIT2IFF Using New EDIT2IFF Method |
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213 | (2) |
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215 | (2) |
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217 | (88) |
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217 | (10) |
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217 | (2) |
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Three-Way Sub-sampling Cross Validation Method |
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219 | (2) |
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Measuring Models' Prediction Performance |
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221 | (1) |
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Performance Evaluations of Regression Experiments |
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221 | (2) |
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Performance Evaluations of Classification Experiments |
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223 | (4) |
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Parameters of Benchmark Algorithms |
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227 | (7) |
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Support Vector Machines (SVM) |
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228 | (1) |
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Artificial Neural Networks (NN) |
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229 | (1) |
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Adaptive-Network-Based Fuzzy Inference System (ANFIS) |
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229 | (2) |
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Dynamically Evolving Neuro-Fuzzy Inference Method (DENFIS) |
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231 | (1) |
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Discrete Interval Valued Type-2 Fuzzy Rule Base (DIT2FRB) |
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231 | (1) |
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Genetic Fuzzy System (GFS) |
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232 | (2) |
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Logistic Regression, LR, Fuzzy K-Nearest Neighbor, FKNN |
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234 | (1) |
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Parameters of Proposed Fuzzy Functions Algorithms |
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234 | (4) |
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234 | (2) |
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Imporoved Fuzzy Functions Methods |
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236 | (2) |
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Analysis of Experiments - Regression Domain |
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238 | (40) |
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Friedman's Artificial Domain |
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238 | (7) |
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245 | (6) |
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Desulphurization Process Dataset |
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251 | (11) |
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262 | (14) |
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Proposed Fuzzy Cluster Validity Index Analysis for Regression |
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276 | (2) |
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Analysis of Experiments - Classification (Pattern Recognition) Domains |
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278 | (11) |
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Classification Datasets from UCI Repository |
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279 | (2) |
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Classification Dataset from StatLib |
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281 | (1) |
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Results from Classification Datasets |
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281 | (2) |
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Proposed Fuzzy Cluster Validity Index Analysis for Classification |
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283 | (1) |
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Performance Comparison Based on Elapsed Times |
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284 | (5) |
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Overall Discussions on Experiments |
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289 | (11) |
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Overall Comparison of System Modeling Methods on Regression Datasets |
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290 | (7) |
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Overall Comparison of System Modeling Methods on Classification Datasets |
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297 | (3) |
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Summary of Results and Discussions |
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300 | (5) |
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Conclusions and Future Work |
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305 | (8) |
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305 | (5) |
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310 | (3) |
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313 | (8) |
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321 | |
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A.1 Set and Logic Theory - Additional Information |
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321 | (1) |
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A.2 Fuzzy Relations (Composition) - An Example |
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322 | (1) |
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B.1 Proof of Fuzzy c-Means Clustering Algorithm |
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323 | (3) |
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B.2 Proof of Improved Fuzzy Clustering Algorithm |
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326 | (1) |
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C.1 Artificial Neural Networks ANNs) |
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327 | (2) |
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C.2 Support Vector Machines |
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329 | (9) |
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338 | (2) |
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C.4 Multiple Linear Regression Algorithms with Least Squares Estimation |
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340 | (1) |
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341 | (2) |
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C.6 Fuzzy K-Nearest Neighbor Approach |
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343 | (1) |
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344 | (1) |
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D.2 Friedman's Artificial Dataset: Summary of Results |
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345 | (9) |
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D.3 Auto-mileage Dataset: Summary of Results |
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354 | (9) |
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D.4 Desulphurization Dataset: Summary of Results |
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363 | (4) |
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D.5 Stock Price Datasets: Summary of Results |
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367 | (11) |
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D.6 Classification Datasets: Summary of Results |
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388 | (9) |
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D.7 Cluster Validity Index Graphs |
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397 | (1) |
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D.8 Classification Datasets - ROC Graphs |
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398 | |