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Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence 2007 ed. [Kõva köide]

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  • Formaat: Hardback, 311 pages, kõrgus x laius: 235x155 mm, kaal: 664 g, 87 Illustrations, black and white; XVI, 311 p. 87 illus., 1 Hardback
  • Sari: Natural Computing Series
  • Ilmumisaeg: 23-Apr-2007
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540496068
  • ISBN-13: 9783540496069
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Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence 2007 ed.Suurem pilt
  • Formaat: Hardback, 311 pages, kõrgus x laius: 235x155 mm, kaal: 664 g, 87 Illustrations, black and white; XVI, 311 p. 87 illus., 1 Hardback
  • Sari: Natural Computing Series
  • Ilmumisaeg: 23-Apr-2007
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540496068
  • ISBN-13: 9783540496069
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783540496069.html
Märksõnad:
"This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains." "This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit."--BOOK JACKET.



Arvustused

"This book tries to balance the mixture of theories, algorithms, and applications and is a good reference for people who want to solve a complex optimization problem for their field. ... Overall, this book is well organized and well written. There is no doubt that this is another good pattern recognition reference to have on one's bookshelf." (Zheng Liu, IAPR Newsletter 30(4), October 2008)

Introduction
1(18)
Introduction
1(2)
Machine Recognition of Patterns: Preliminaries
3(6)
Data Acquisition
4(1)
Feature Selection
5(1)
Classification
6(2)
Clustering
8(1)
Different Approaches
9(2)
Connectionist Approach: Relevance and Features
11(2)
Genetic Approach: Relevance and Features
13(1)
Fuzzy Set-Theoretic Approach: Relevance and Features
14(1)
Other Approaches
15(1)
Applications of Pattern Recognition and Learning
16(1)
Summary and Scope of the Book
17(2)
Genetic Algorithms
19(34)
Introduction
19(1)
Traditional Versus Nontraditional Search
19(2)
Overview of Genetic Algorithms
21(8)
Basic Principles and Features
21(1)
Encoding Strategy and Population
22(2)
Evaluation
24(1)
Genetic Operators
24(3)
Parameters of Genetic Algorithms
27(1)
Schema Theorem
27(2)
Proof of Convergence of GAs
29(6)
Markov Chain Modelling of GAs
29(2)
Limiting Behavior of Elitist Model of GAs
31(4)
Some Implementation Issues in GAs
35(5)
Multiobjective Genetic Algorithms
40(6)
Applications of Genetic Algorithms
46(5)
Summary
51(2)
Supervised Classification Using Genetic Algorithms
53(28)
Introduction
53(1)
Genetic Algorithms for Generating Fuzzy If--Then Rules
54(3)
Genetic Algorithms and Decision Trees
57(3)
GA-classifier: Genetic Algorithm for Generation of Class Boundaries
60(5)
Principle of Hyperplane Fitting
61(1)
Region Identification and Fitness Computation
62(3)
Genetic Operations
65(1)
Experimental Results
65(13)
Results
69(6)
Consideration of Higher-Order Surfaces
75(3)
Summary
78(3)
Theoretical Analysis of the GA-classifier
81(28)
Introduction
81(1)
Relationship with Bayes' Error Probability
82(6)
Relationship Between H opt and H GA
88(2)
Obtaining HGA from H
88(1)
How H GA Is Related to H opt
89(1)
Some Points Related to n and H
89(1)
Experimental Results
90(16)
Data Sets
91(2)
Learning the Class Boundaries and Performance on Test Data
93(11)
Variation of Recognition Scores with P1
104(2)
Summary
106(3)
Variable String Lengths in GA-classifier
109(30)
Introduction
109(1)
Genetic Algorithm with Variable String Length and the Classification Criteria
110(1)
Description of VGA-Classifier
111(6)
Chromosome Representation and Population Initialization
111(2)
Fitness Computation
113(1)
Genetic Operators
114(3)
Theoretical Study of VGA-classifier
117(2)
Issues of Minimum miss and H
117(1)
Error Rate
118(1)
Experimental Results
119(5)
Data Sets
119(1)
Results
120(4)
VGA-classifier for the Design of a Multilayer Perceptron
124(8)
Analogy Between Multilayer Perceptron and VGA-classifier
124(1)
Deriving the MLP Architecture and the Connection Weights
125(4)
Postprocessing Step
129(2)
Experimental Results
131(1)
Summary
132(7)
Chromosome Differentiation in VGA-classifier
139(20)
Introduction
139(1)
GACD: Incorporating Chromosome Differentiation in GA
140(3)
Motivation
140(1)
Description of GACD
140(3)
Schema Theorem for GACD
143(5)
Terminology
143(1)
Analysis of GACD
143(5)
VGACD-classifier: Incorporation of Chromosome Differentiation in VGA-classifier
148(2)
Population Initialization
149(1)
Fitness Computation and Genetic Operators
150(1)
Pixel Classification of Remotely Sensed Image
150(4)
Relevance of GA
150(1)
Experimental Results
150(4)
Summary
154(5)
Multiobjective VGA-classifier and Quantitative Indices
159(22)
Introduction
159(1)
Multiobjective Optimization
160(1)
Relevance of Multiobjective Optimization
161(1)
Multiobjective GA-Based Classifier
162(7)
Chromosome Representation
162(1)
Fitness Computation
162(1)
Selection
163(1)
Crossover
164(1)
Mutation
164(1)
Incorporating Elitism
165(2)
PAES-classifier: The Classifier Based on Pareto Archived Evolution Strategy
167(2)
Validation and Testing
169(1)
Indices for Comparing MO Solutions
170(2)
Measures Based on Position of Nondominated Front
170(1)
Measures Based on Diversity of the Solutions
171(1)
Experimental Results
172(7)
Parameter Values
173(1)
Comparison of Classification Performance
173(6)
Summary
179(2)
Genetic Algorithms in Clustering
181(32)
Introduction
181(1)
Basic Concepts and Preliminary Definitions
182(2)
Clustering Algorithms
184(3)
K-Means Clustering Algorithm
184(1)
Single-Linkage Clustering Algorithm
185(1)
Fuzzy c-Means Clustering Algorithm
186(1)
Clustering Using GAs: Fixed Number of Crisp Clusters
187(5)
Encoding Strategy
188(1)
Population Initialization
188(1)
Fitness Computation
188(1)
Genetic Operators
189(1)
Experimental Results
189(3)
Clustering Using GAs: Variable Number of Crisp Clusters
192(13)
Encoding Strategy and Population Initialization
192(1)
Fitness Computation
193(1)
Genetic Operators
193(1)
Some Cluster Validity Indices
194(2)
Experimental Results
196(9)
Clustering Using GAs: Variable Number of Fuzzy Clusters
205(7)
Fitness Computation
205(1)
Experimental Results
206(6)
Summary
212(1)
Genetic Learning in Bioinformatics
213(30)
Introduction
213(1)
Bioinformatics: Concepts and Features
214(2)
Basic Concepts of Cell Biology
214(2)
Different Bioinformatics Tasks
216(1)
Relevance of Genetic Algorithms in Bioinformatics
216(4)
Bioinformatics Tasks and Application of GAs
220(18)
Alignment and Comparison of DNA, RNA and Protein Sequences
220(3)
Gene Mapping on Chromosomes
223(1)
Gene Finding and Promoter Identification from DNA Sequences
224(2)
Interpretation of Gene Expression and Microarray Data
226(1)
Gene Regulatory Network Identification
227(1)
Construction of Phylogenetic Trees for Studying Evolutionary Relationship
228(1)
DNA Structure Prediction
229(2)
RNA Structure Prediction
231(2)
Protein Structure Prediction and Classification
233(3)
Molecular Design and Docking
236(2)
Experimental Results
238(1)
Summary
239(4)
Genetic Algorithms and Web Intelligence
243(14)
Introduction
243(1)
Web Mining
244(6)
Web Mining Components and Methodologies
246(1)
Web Mining Categories
246(2)
Challenges and Limitations in Web Mining
248(2)
Genetic Algorithms in Web Mining
250(5)
Search and Retrieval
250(2)
Query Optimization and Reformulation
252(2)
Document Representation and Personalization
254(1)
Distributed Mining
254(1)
Summary
255(2)
ε-Optimal Stopping Time for GAs
257(12)
Introduction
257(1)
Foundation
257(2)
Fitness Function
259(2)
Upper Bound for Optimal Stopping Time
261(3)
Mutation Probability and ε-Optimal Stopping Time
264(5)
Data Sets Used for the Experiments
269(6)
Variation of Error Probability with P1
275(2)
References 277(32)
Index 309