LEADER 05087nam 2200673Ia 450 001 9910784998903321 005 20230828213040.0 010 $a1-281-37912-3 010 $a9786611379124 010 $a981-277-392-4 035 $a(CKB)1000000000407569 035 $a(EBL)1679470 035 $a(OCoLC)815571423 035 $a(SSID)ssj0000098335 035 $a(PQKBManifestationID)11988629 035 $a(PQKBTitleCode)TC0000098335 035 $a(PQKBWorkID)10132186 035 $a(PQKB)11380382 035 $a(MiAaPQ)EBC1679470 035 $a(WSP)00006072 035 $a(Au-PeEL)EBL1679470 035 $a(CaPaEBR)ebr10201443 035 $a(CaONFJC)MIL137912 035 $a(EXLCZ)991000000000407569 100 $a20061019d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAdvances in computational intelligence$b[electronic resource] $etheory & applications /$fFei-Yue Wang, Derong Liu 210 $aToh Tuck Link, Singapore ;$aNew Jersey $cWorld Scientific$dc2006 215 $a1 online resource (478 p.) 225 1 $aSeries in intelligent control and intelligent automation ;$vv. 5 300 $aDescription based upon print version of record. 311 $a981-256-734-8 320 $aIncludes bibliographical references and index. 327 $aContents ; Preface ; List of Contributors ; 1 A Quest for Granular Computing and Logic Processing ; 1.1 Introduction ; 1.2 Granular Computing ; 1.3 Granular Computing and Logic: Synergistic Links ; 1.4 Main Categories of Fuzzy Logic Processing Units 327 $a1.5 A General Topology of the Network 1.6 Interpretation Issues of Logic Networks ; 1.7 Conclusions ; Bibliography ; 2 Abstraction and Linguistic Analysis of Conventional Numerical Dynamic Systems ; 2.1 Introduction ; 2.2 Type-I Linguistic Dynamic Systems 327 $a2.3 Type-II Linguistic Dynamic Systems 2.4 Linguistic Control Design for Goal States Specified in Words ; 2.5 Conclusions ; Bibliography ; 3 Slicing: A Distributed Learning Approach ; 3.1 Introduction ; 3.2 Slicing ; 3.3 Variance Reduction in Slicing ; 3.4 Experiments 327 $a3.5 Analysis 3.6 Discussion ; 3.7 Conclusions ; Bibliography ; 4 Marginal Learning Algorithms in Statistical Machine Learning ; 4.1 Introduction ; 4.2 Classification Problems and Margin ; 4.3 Maximal Margin Algorithm in SVM 327 $a4.4 Unbalanced Classification Problems and Weighted Maximal Margin Algorithms 4.5 The n-Unsupervised Learning Problems and Margin ; 4.6 Some Marginal Algorithms for One-Class Problems ; 4.7 Some New Algorithms of Clustering Problems ; 4.8 New Marginal Algorithms for PCA 327 $a4.9 Conclusions 330 $a Computational Intelligence (CI) is a recently emerging area in fundamental and applied research, exploiting a number of advanced information processing technologies that mainly embody neural networks, fuzzy logic and evolutionary computation. With a major concern to exploiting the tolerance for imperfection, uncertainty, and partial truth to achieve tractability, robustness and low solution cost, it becomes evident that composing methods of CI should be working concurrently rather than separately. It is this conviction that research on the synergism of CI paradigms has experienced significant 410 0$aSeries in intelligent control and intelligent automation ;$vv. 5. 606 $aArtificial intelligence 606 $aComputational intelligence 615 0$aArtificial intelligence. 615 0$aComputational intelligence. 676 $a006.3 700 $aWang$b Fei-Yue$01534217 701 $aLiu$b Derong$066913 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784998903321 996 $aAdvances in computational intelligence$93781580 997 $aUNINA