{"product_id":"computationally-intelligent-hybrid-systems-the-fusion-of-soft-computing-and-hard-computing-hardback-9780471476689","title":"Computationally Intelligent Hybrid Systems; The Fusion of Soft Computing and Hard Computing (Hardback) 9780471476689","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eComputationally Intelligent Hybrid Systems\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eThe Fusion of Soft Computing and Hard Computing\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eSeppo J. Ovaska (Edited by), SJ Ovaska (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471476689, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 23 November 2004\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e440 pages, Photos: 5 B\u0026amp;W, 0 Color; Drawings: 150 B\u0026amp;W, 0 Color; Tables: 25 B\u0026amp;W, 0 Color\u003cbr\u003e24.3 x 16.1 x 2.5 cm, 0.753 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"…the only book to examine the practical issue involved in the creation of high-performance, cost-effective applications using a synthesis of neural networks, fuzzy systems and evolutionary computation with traditional computing methods.\" (\u003ci\u003eInternational Journal of General Systems\u003c\/i\u003e, June 2005)  \u003cp\u003e\"...accessible for practical engineers and at the same time quite interesting for theoretical computer scientists...it will inspire more people to use the (currently under-utilized) fusion techniques.\" (J\u003ci\u003eournal of Intelligent \u0026amp; Fuzzy Systems\u003c\/i\u003e, Vol. 16, No. 3, 2005)\u003c\/p\u003e \u003cp\u003e\"…these well-written papers serve to offer insight into the powerful combination of soft and hard computing that is now being applied…to real-world applications.\" (\u003ci\u003eComputing Reviews.com\u003c\/i\u003e, June 10, 2005)\u003c\/p\u003e \u003cp\u003e\"This is the first book to treat the subject. With this work, the editor hopes to bridge the gap between the proponents of soft computing and hard computing.\" (\u003ci\u003eE-STREAMS\u003c\/i\u003e, May 2005)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis uniquely crafted work combines the experience of many internationally recognized experts in the soft- and hard-computing research worlds to present practicing engineers with the broadest possible array of methodologies for developing innovative and competitive solutions to real-world problems. Each of the chapters illustrates the wide-ranging applicability of the fusion concept in such critical areas as  \u003cul\u003e \u003cli\u003eComputer security and data mining\u003c\/li\u003e \u003cli\u003eElectrical power systems and large-scale plants\u003c\/li\u003e \u003cli\u003eMotor drives and tool wear monitoring\u003c\/li\u003e \u003cli\u003eUser interfaces and the World Wide Web\u003c\/li\u003e \u003cli\u003eAerospace and robust control\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis must-have guide for practicing engineers, researchers, and R\u0026amp;D managers who wish to create or understand computationally intelligent hybrid systems is also an excellent primary source for graduate courses in soft computing, engineering applications of artificial intelligence, and related topics.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eContributors xv\u003c\/p\u003e \u003cp\u003eForeword xvii\u003cbr\u003e\u003ci\u003eDavid B. Fogel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 1 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Fusion of Soft Computing and Hard Computing 5\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSeppo J. Ovaska\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 5\u003c\/p\u003e \u003cp\u003e1.1.1 Soft Computing 5a\u003c\/p\u003e \u003cp\u003e1.1.2 Fusion of Soft-Computing and Hard-Computing Methodologies 7\u003c\/p\u003e \u003cp\u003e1.2 Structural Categories 9\u003c\/p\u003e \u003cp\u003e1.2.1 Soft Computing and Hard Computing Are Isolated from Each Other 10\u003c\/p\u003e \u003cp\u003e1.2.2 Soft Computing and Hard Computing Are Connected in Parallel 11\u003c\/p\u003e \u003cp\u003e1.2.3 Soft Computing with Hard-Computing Feedback and Hard Computing with Soft-Computing Feedback 12\u003c\/p\u003e \u003cp\u003e1.2.4 Soft Computing is Cascaded with Hard Computing or Hard Computing is Cascaded with Soft Computing 12\u003c\/p\u003e \u003cp\u003e1.2.5 Soft-Computing-Designed Hard Computing and Hard-Computing-Designed Soft Computing 13\u003c\/p\u003e \u003cp\u003e1.2.6 Hard-Computing-Augmented Soft Computing and Soft-Computing-Augmented Hard Computing 14\u003c\/p\u003e \u003cp\u003e1.2.7 Hard-Computing-Assisted Soft Computing and Soft-Computing-Assisted Hard Computing 15\u003c\/p\u003e \u003cp\u003e1.2.8 Supplementary Categories 16\u003c\/p\u003e \u003cp\u003e1.2.9 General Soft-Computing and Hard-Computing Mapping Functions 19\u003c\/p\u003e \u003cp\u003e1.3 Characteristic Features 19\u003c\/p\u003e \u003cp\u003e1.3.1 Proportional Integral Derivative Controllers 20\u003c\/p\u003e \u003cp\u003e1.3.2 Physical Models 20\u003c\/p\u003e \u003cp\u003e1.3.3 Optimization Utilizing Local Information 21\u003c\/p\u003e \u003cp\u003e1.3.4 General Parameter Adaptation Algorithm 22\u003c\/p\u003e \u003cp\u003e1.3.5 Stochastic System Simulators 22\u003c\/p\u003e \u003cp\u003e1.3.6 Discussion and Extended Fusion Schemes 22\u003c\/p\u003e \u003cp\u003e1.4 Characterization of Hybrid Applications 24\u003c\/p\u003e \u003cp\u003e1.5 Conclusions and Discussion 25\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 2 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 General Model for Large-Scale Plant Application 35\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAkimoto Kamiya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 35\u003c\/p\u003e \u003cp\u003e2.2 Control System Architecture 36\u003c\/p\u003e \u003cp\u003e2.3 Forecasting of Market Demand 37\u003c\/p\u003e \u003cp\u003e2.4 Scheduling of Processes 39\u003c\/p\u003e \u003cp\u003e2.4.1 Problem Decomposition 39\u003c\/p\u003e \u003cp\u003e2.4.2 Hybrid Genetic Algorithms 42\u003c\/p\u003e \u003cp\u003e2.4.3 Multiobjective Optimization 43\u003c\/p\u003e \u003cp\u003e2.5 Supervisory Control 45\u003c\/p\u003e \u003cp\u003e2.6 Local Control 47\u003c\/p\u003e \u003cp\u003e2.7 General Fusion Model and Fusion Categories 49\u003c\/p\u003e \u003cp\u003e2.8 Conclusions 51\u003c\/p\u003e \u003cp\u003eReferences 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 3 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Adaptive Flight Control: Soft Computing with Hard Constraints 61\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRichard E. Saeks\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 61\u003c\/p\u003e \u003cp\u003e3.2 The Adaptive Control Algorithms 62\u003c\/p\u003e \u003cp\u003e3.2.1 Adaptive Dynamic Programming 63\u003c\/p\u003e \u003cp\u003e3.2.2 Neural Adaptive Control 64\u003c\/p\u003e \u003cp\u003e3.3 Flight Control 67\u003c\/p\u003e \u003cp\u003e3.4 X-43A-LS Autolander 68\u003c\/p\u003e \u003cp\u003e3.5 LOFLYTEw Optimal Control 73\u003c\/p\u003e \u003cp\u003e3.6 LOFLYTEw Stability Augmentation 76\u003c\/p\u003e \u003cp\u003e3.7 Design for Uncertainty with Hard Constraints 82\u003c\/p\u003e \u003cp\u003e3.8 Fusion of Soft Computing and Hard Computing 85\u003c\/p\u003e \u003cp\u003e3.9 Conclusions 85\u003c\/p\u003e \u003cp\u003eReferences 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 4 89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Sensorless Control of Switched Reluctance Motors 93\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAdrian David Cheok\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 93\u003c\/p\u003e \u003cp\u003e4.2 Fuzzy Logic Model 95\u003c\/p\u003e \u003cp\u003e4.2.1 Measurement of Flux Linkage Characteristics 95\u003c\/p\u003e \u003cp\u003e4.2.2 Training and Validation of Fuzzy Model 97\u003c\/p\u003e \u003cp\u003e4.3 Accuracy Enhancement Algorithms 101\u003c\/p\u003e \u003cp\u003e4.3.1 Soft-Computing-Based Optimal Phase Selection 102\u003c\/p\u003e \u003cp\u003e4.3.2 Hard-Computing-Based On-Line Resistance Estimation 104\u003c\/p\u003e \u003cp\u003e4.3.3 Polynomial Predictive Filtering 105\u003c\/p\u003e \u003cp\u003e4.4 Simulation Algorithm and Results 108\u003c\/p\u003e \u003cp\u003e4.5 Hardware and Software Implementation 109\u003c\/p\u003e \u003cp\u003e4.5.1 Hardware Configuration 109\u003c\/p\u003e \u003cp\u003e4.5.2 Software Implementation 110\u003c\/p\u003e \u003cp\u003e4.6 Experimental Results 111\u003c\/p\u003e \u003cp\u003e4.6.1 Acceleration from Zero Speed 112\u003c\/p\u003e \u003cp\u003e4.6.2 Low-Current Low-Speed Test 113\u003c\/p\u003e \u003cp\u003e4.6.3 High-Speed Test 114\u003c\/p\u003e \u003cp\u003e4.6.4 Test of Step Change of Load 118\u003c\/p\u003e \u003cp\u003e4.7 Fusion of Soft Computing and Hard Computing 119\u003c\/p\u003e \u003cp\u003e4.8 Conclusion and Discussion 122\u003c\/p\u003e \u003cp\u003eReferences 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 5 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Estimation of Uncertainty Bounds for Linear and Nonlinear Robust Control 129\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGregory D. Buckner\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 129\u003c\/p\u003e \u003cp\u003e5.2 Robust Control of Active Magnetic Bearings 130\u003c\/p\u003e \u003cp\u003e5.2.1 Active Magnetic Bearing Test Rig 132\u003c\/p\u003e \u003cp\u003e5.3 Nominal H1 Control of the AMB Test Rig 133\u003c\/p\u003e \u003cp\u003e5.3.1 Parametric System Identification 133\u003c\/p\u003e \u003cp\u003e5.3.2 Uncertainty Bound Specification 135\u003c\/p\u003e \u003cp\u003e5.3.3 Nominal H1 Control: Experimental Results 137\u003c\/p\u003e \u003cp\u003e5.4 Estimating Modeling Uncertainty for H1 Control of the AMB Test Rig 138\u003c\/p\u003e \u003cp\u003e5.4.1 Model Error Modeling 140\u003c\/p\u003e \u003cp\u003e5.4.2 Intelligent Model Error Identification 141\u003c\/p\u003e \u003cp\u003e5.4.3 Uncertainty Bound Specification 146\u003c\/p\u003e \u003cp\u003e5.4.4 Identified H1 Control: Experimental Results 147\u003c\/p\u003e \u003cp\u003e5.5 Nonlinear Robust Control of the AMB Test Rig 148\u003c\/p\u003e \u003cp\u003e5.5.1 Nominal Sliding Mode Control of the AMB Test Rig 148\u003c\/p\u003e \u003cp\u003e5.5.2 Nominal SMC: Experimental Results 150\u003c\/p\u003e \u003cp\u003e5.6 Estimating Model Uncertainty for SMC of the AMB Test Rig 151\u003c\/p\u003e \u003cp\u003e5.6.1 Intelligent System Identification 151\u003c\/p\u003e \u003cp\u003e5.6.2 Intelligent Model Error Identification 155\u003c\/p\u003e \u003cp\u003e5.6.3 Intelligent SMC: Experimental Results 156\u003c\/p\u003e \u003cp\u003e5.7 Fusion of Soft Computing and Hard Computing 159\u003c\/p\u003e \u003cp\u003e5.8 Conclusion 162\u003c\/p\u003e \u003cp\u003eReferences 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 6 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Indirect On-Line Tool Wear Monitoring 169\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBernhard Sick\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 169\u003c\/p\u003e \u003cp\u003e6.2 Problem Description and Monitoring Architecture 172\u003c\/p\u003e \u003cp\u003e6.3 State of the Art 176\u003c\/p\u003e \u003cp\u003e6.3.1 Monitoring Techniques Based on Analytical Models 176\u003c\/p\u003e \u003cp\u003e6.3.2 Monitoring Techniques Based on Neural Networks 178\u003c\/p\u003e \u003cp\u003e6.3.3 Monitoring Techniques Based on Fusion of Physical and Neural Network Models 181\u003c\/p\u003e \u003cp\u003e6.4 New Solution 184\u003c\/p\u003e \u003cp\u003e6.4.1 Solution Outline 184\u003c\/p\u003e \u003cp\u003e6.4.2 Physical Force Model at Digital Preprocessing Level 185\u003c\/p\u003e \u003cp\u003e6.4.3 Dynamic Neural Network at Wear Model Level 187\u003c\/p\u003e \u003cp\u003e6.5 Experimental Results 189\u003c\/p\u003e \u003cp\u003e6.6 Fusion of Soft Computing and Hard Computing 192\u003c\/p\u003e \u003cp\u003e6.7 Summary and Conclusions 194\u003c\/p\u003e \u003cp\u003eReferences 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 7 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Predictive Filtering Methods for Power Systems Applications 203\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSeppo J. Ovaska\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 203\u003c\/p\u003e \u003cp\u003e7.2 Multiplicative General-Parameter Filtering 205\u003c\/p\u003e \u003cp\u003e7.3 Genetic Algorithm for Optimizing Filter Tap Cross-Connections 207\u003c\/p\u003e \u003cp\u003e7.4 Design of Multiplierless Basis Filters by Evolutionary Programming 211\u003c\/p\u003e \u003cp\u003e7.5 Predictive Filters for Zero-Crossings Detector 213\u003c\/p\u003e \u003cp\u003e7.5.1 Single 60-Hz Sinusoid Corrupted by Noise 213\u003c\/p\u003e \u003cp\u003e7.5.2 Sequence of 49-, 50-, and 51-Hz Sinusoids Corrupted by Noise 217\u003c\/p\u003e \u003cp\u003e7.5.3 Discussion of Zero-Crossings Detection Application 222\u003c\/p\u003e \u003cp\u003e7.6 Predictive Filters for Current Reference Generators 223\u003c\/p\u003e \u003cp\u003e7.6.1 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids 225\u003c\/p\u003e \u003cp\u003e7.6.2 Sequence of 49-, 50-, and 51-Hz Noisy Sinusoids Corrupted by Harmonics 229\u003c\/p\u003e \u003cp\u003e7.6.3 Artificial Current Signal Corrupted by Odd Harmonics 230\u003c\/p\u003e \u003cp\u003e7.6.4 Discussion of Current Reference Generation Application 232\u003c\/p\u003e \u003cp\u003e7.7 Fusion of Soft Computing and Hard Computing 233\u003c\/p\u003e \u003cp\u003e7.8 Conclusion 234\u003c\/p\u003e \u003cp\u003eReferences 237\u003c\/p\u003e \u003cp\u003eAppendix 7.1: Coefficients of 50-Hz Sinusoid-Predictive FIR Filters 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 8 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Intrusion Detection for Computer Security 245\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSung-Bae Cho and Sang-Jun Han\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 245\u003c\/p\u003e \u003cp\u003e8.2 Related Works 247\u003c\/p\u003e \u003cp\u003e8.2.1 Neural Computing 248\u003c\/p\u003e \u003cp\u003e8.2.2 Genetic Computing 249\u003c\/p\u003e \u003cp\u003e8.2.3 Fuzzy Logic 251\u003c\/p\u003e \u003cp\u003e8.2.4 Probabilistic Reasoning 253\u003c\/p\u003e \u003cp\u003e8.3 Intrusion Detection with Hybrid Techniques 253\u003c\/p\u003e \u003cp\u003e8.3.1 Overview 254\u003c\/p\u003e \u003cp\u003e8.3.2 Preprocessing with Self-Organizing Map 254\u003c\/p\u003e \u003cp\u003e8.3.3 Behavior Modeling with Hidden Markov Models 256\u003c\/p\u003e \u003cp\u003e8.3.4 Multiple Models Fusion by Fuzzy Logic 259\u003c\/p\u003e \u003cp\u003e8.4 Experimental Results 261\u003c\/p\u003e \u003cp\u003e8.4.1 Preprocessing 261\u003c\/p\u003e \u003cp\u003e8.4.2 Modeling and Intrusion Detection 263\u003c\/p\u003e \u003cp\u003e8.5 Fusion of Soft Computing and Hard Computing 267\u003c\/p\u003e \u003cp\u003e8.6 Concluding Remarks 268\u003c\/p\u003e \u003cp\u003eReferences 270\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 9 273\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Emotion Generating Method On Human–Computer Interfaces 277\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKazuya Mera and Takumi Ichimura\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 277\u003c\/p\u003e \u003cp\u003e9.2 Emotion Generating Calculations Method 279\u003c\/p\u003e \u003cp\u003e9.2.1 Favorite Value Database 280\u003c\/p\u003e \u003cp\u003e9.2.2 Calculation Pleasure\/Displeasure for an Event 282\u003c\/p\u003e \u003cp\u003e9.2.3 Favorite Value of Modified Element 284\u003c\/p\u003e \u003cp\u003e9.2.4 Experimental Result 285\u003c\/p\u003e \u003cp\u003e9.2.5 Complicated Emotion Allocating Method 286\u003c\/p\u003e \u003cp\u003e9.2.6 Dependency Among Emotion Groups 294\u003c\/p\u003e \u003cp\u003e9.2.7 Example of Complicated Emotion Allocating Method 296\u003c\/p\u003e \u003cp\u003e9.2.8 Experimental Results 297\u003c\/p\u003e \u003cp\u003e9.3 Emotion-Oriented Interaction Systems 298\u003c\/p\u003e \u003cp\u003e9.3.1 Facial Expression Generating Method by Neural Network 298\u003c\/p\u003e \u003cp\u003e9.3.2 Assign Rules to the Facial Expressions 301\u003c\/p\u003e \u003cp\u003e9.4 Applications of Emotion-Oriented Interaction Systems 302\u003c\/p\u003e \u003cp\u003e9.4.1 JavaFaceMail 302\u003c\/p\u003e \u003cp\u003e9.4.2 JavaFaceChat 307\u003c\/p\u003e \u003cp\u003e9.5 Fusion of Soft Computing and Hard Computing 308\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 310\u003c\/p\u003e \u003cp\u003eReferences 311\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 10 313\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Introduction to Scientific Data Mining: Direct Kernel Methods and Applications 317\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMark J. Embrechts, Boleslaw Szymanski, and Karsten Sternickel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 317\u003c\/p\u003e \u003cp\u003e10.2 What is Data Mining? 318\u003c\/p\u003e \u003cp\u003e10.2.1 Introduction to Data Mining 318\u003c\/p\u003e \u003cp\u003e10.2.2 Scientific Data Mining 320\u003c\/p\u003e \u003cp\u003e10.2.3 The Data Mining Process 321\u003c\/p\u003e \u003cp\u003e10.2.4 Data Mining Methods and Techniques 322\u003c\/p\u003e \u003cp\u003e10.3 Basic Definitions for Data Mining 323\u003c\/p\u003e \u003cp\u003e10.3.1 The MetaNeural Data Format 323\u003c\/p\u003e \u003cp\u003e10.3.2 The “Standard Data Mining Problem” 326\u003c\/p\u003e \u003cp\u003e10.3.3 Predictive Data Mining 329\u003c\/p\u003e \u003cp\u003e10.3.4 Metrics for Assessing Model Quality 333\u003c\/p\u003e \u003cp\u003e10.4 Introduction to Direct Kernel Methods 335\u003c\/p\u003e \u003cp\u003e10.4.1 Data Mining and Machine Learning Dilemmas for Real-World Data 335\u003c\/p\u003e \u003cp\u003e10.4.2 Regression Models Based on the Data Kernel 338\u003c\/p\u003e \u003cp\u003e10.4.3 Kernel Transformations 339\u003c\/p\u003e \u003cp\u003e10.4.4 Dealing with Bias: Centering the Kernel 340\u003c\/p\u003e \u003cp\u003e10.5 Direct Kernel Ridge Regression 342\u003c\/p\u003e \u003cp\u003e10.5.1 Overview 342\u003c\/p\u003e \u003cp\u003e10.5.2 Choosing the Ridge Parameter 343\u003c\/p\u003e \u003cp\u003e10.6 Case Study #1: Predicting the Binding Energy for Amino Acids 344\u003c\/p\u003e \u003cp\u003e10.7 Case Study #2: Predicting the Region of Origin for Italian Olive Oils 346\u003c\/p\u003e \u003cp\u003e10.8 Case Study #3: Predicting Ischemia from Magnetocardiography 350\u003c\/p\u003e \u003cp\u003e10.8.1 Introduction to Magnetocardiography 350\u003c\/p\u003e \u003cp\u003e10.8.2 Data Acquisition and Preprocessing 351\u003c\/p\u003e \u003cp\u003e10.8.3 Predictive Modeling for Binary Classification of Magnetocardiograms 351\u003c\/p\u003e \u003cp\u003e10.8.4 Feature Selection 358\u003c\/p\u003e \u003cp\u003e10.9 Fusion of Soft Computing and Hard Computing 359\u003c\/p\u003e \u003cp\u003e10.10 Conclusions 359\u003c\/p\u003e \u003cp\u003eReferences 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003eEditor’s Introduction to Chapter 11 363\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 World Wide Web Usage Mining 367\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAjith Abraham\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 367\u003c\/p\u003e \u003cp\u003e11.2 Daily and Hourly Web Usage Clustering 372\u003c\/p\u003e \u003cp\u003e11.2.1 Ant Colony Optimization 372\u003c\/p\u003e \u003cp\u003e11.2.2 Fuzzy Clustering Algorithm 374\u003c\/p\u003e \u003cp\u003e11.2.3 Self-Organizing Map 376\u003c\/p\u003e \u003cp\u003e11.2.4 Analysis of Web Data Clusters 377\u003c\/p\u003e \u003cp\u003e11.3 Daily and Hourly Web Usage Analysis 378\u003c\/p\u003e \u003cp\u003e11.3.1 Linear Genetic Programming 379\u003c\/p\u003e \u003cp\u003e11.3.2 Fuzzy Inference Systems 382\u003c\/p\u003e \u003cp\u003e11.3.3 Experimentation Setup, Training, and Performance Evaluation 387\u003c\/p\u003e \u003cp\u003e11.4 Fusion of Soft Computing and Hard Computing 389\u003c\/p\u003e \u003cp\u003e11.5 Conclusions 393\u003c\/p\u003e \u003cp\u003eReferences 394\u003c\/p\u003e \u003cp\u003eIndex 397\u003c\/p\u003e \u003cp\u003eAbout the Editor 409\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Brand New","offer_id":52293486575896,"sku":"9780471476689","price":106.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471476689.jpg?v=1781641854","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/computationally-intelligent-hybrid-systems-the-fusion-of-soft-computing-and-hard-computing-hardback-9780471476689","provider":"Freshly Printed Books","version":"1.0","type":"link"}