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Wong M.L., Leung K.S. — Data mining using grammar based genetic programming and applications
Wong M.L., Leung K.S. — Data mining using grammar based genetic programming and applications



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Название: Data mining using grammar based genetic programming and applications

Авторы: Wong M.L., Leung K.S.

Аннотация:

Data mining involves the non-trivial extraction of implicit, previously unknown, and potentially useful information from databases. Genetic Programming (GP) and Inductive Logic Programming (ILP) are two of the approaches for data mining. This book first sets the necessary backgrounds for the reader, including an overview of data mining, evolutionary algorithms and inductive logic programming. It then describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammars. The formalism is powerful enough to represent context-sensitive information and domain-dependent knowledge. This knowledge can be used to accelerate the learning speed and/or improve the quality of the knowledge induced.


Язык: en

Рубрика: Computer science/Генетика, нейронные сети/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2002

Количество страниц: 213

Добавлена в каталог: 25.11.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
$(\mu +1)-ES$      49
$(\mu +\labbda)-ES$      52
$(\mu,\lambda)-ES$      52
$\theta$-subsumption      65
(1+1)-ES      52
A saturation procedure      62
Absorption      62
Adjusted fitness      45
args      95
Arity      60
atom      60
Atomic formula      60
Background knowledge      59
body      61
Bottom-up ILP systems      64
Canonical Genetic Algorithm      30
Clause      60
Closure property      43
Concept description languages      58
Confidence factor      144
Constant      72
Credit assignment methods      27
Cross-validationprocedure      122
Crossover      81
Crowding factor      147
Cumulative probability of success      107 113
Definite clause grammars      72
Definite goal      61
Definite program      60
Definite program clause      60
Derivation tree      74
Determining coverage      65
Deterministic crowding      147
Difference list approach      76
Discrete recombination operator      50
Distributional bias      38
Diversity      34
Dot product      104
Empirical ILP      62
Encoding length restriction      67
Evolution strategies      48
Evolutionary algorithms      27
Evolutionary programming      53
Exactrule      143
Extensional concepts      58
Extensional coverage      63
Fact      61
Fitness proportionate selection      31
Fitness scaling techniques      35
Fitness sharing      147
Frozen sub-trees      75
Function      60 72
Function symbol      60
Generation gap      147
Genetic algorithms      29
Global discrete recombination operator      50
Global intermediate recombination      51
Global recombination operators      50
Ground formula      61
Ground model      63
Ground term      61
Horn clause      61
Hybrid genetic algorithm      41
ij-determination      65
Inductive concept learning      58
Intensional concepts      58
Intensional coverage      62
Interactive ILP      61
Intermediate recombination operator      50
Intraconstruction      62
Inverse resolution      62
Knowledge-level learning      57
Language bias      58
Laplace estimate      68
Likelihood ratio statistic      69
Linear scaling      35
Literal      60
Logic goals      73
Logic grammar template      102
Logic grammars      72
M-estimate      68
Meta-GAs      40
Most specific inverse resolvent      64
Multi-point crossover      36
Multiple concept learning      58
MUTATE-POINT      96
MUTATED-SUB-TREE      95
Mutation      94
Negation-as-failure      61
Negative literal      60
NEW-BINDINGS      96
NEW-NON-TERMINAL      96
Non-terminal      95
Non-terminal symbols      73
Normal program      61
Normalized confidence factor      144
Number of programs processed      107 113
Object description languages      58
Parse trees      75
Partially Matched Crossover      39
Positional bias      38
Positive literal      60
Positive unit clause      61
Power law scaling      35
Pre-selection      146
Predicate definition      61
Predicate symbol      60
Premature convergence      34
Primary derivation tree      81
Primary parent      81
PRIMARY-SUB-TREES      81
Rank-based selection      35
Raw fitness      45
Refinement operators      61
Relational concept learning      59
Relative fitness      30
Relative least general generalization      64
Remainder stochastic sampling method      34
Roulette wheel selection      32
Search bias      58
Secondary derivation tree      81
Secondary parent      81
SECONDARY-SUB-TREES      82
SEL-PRIMARY-SUB-TREE      82
SEL-SECONDARY-SUB-TREE      82
Siblings      82
Sigma truncation      35
Similarity      147
Simple Genetic Algorithm      3 1
Single concept learning      58
SLD-resolution proof procedure      62
Specialization operator      65
Standardized fitness      45
Steady state genetic algorithm      40
Stochastic Universal Sampling      34
Strong language bias      58
Strong methods      28
Strong search bias      58
Strongly Typed Genetic Programming      47
Strongrule      143
Sub-trees      94
Support      143
Symbol-level learning      57
TEMP-SECONDARY-SUB-TREES      82
Term      60
Terminal symbols      72
Theory      61
Token competition      148
Tournament selection      36
Truncation      62
Two-point crossover      36
Uniform crossover      36
Variable      60 72
Weak language bias      58
Weak methods      27
Weak search bias      58
Weakrule      143
Well-formed formula      61
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