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Friedman M., Kandel A. — Introduction to pattern recognition
Friedman M., Kandel A. — Introduction to pattern recognition

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Название: Introduction to pattern recognition

Авторы: Friedman M., Kandel A.

Аннотация:

This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability theory.


Язык: en

Рубрика: Computer science/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1999

Количество страниц: 329

Добавлена в каталог: 19.03.2006

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID
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Предметный указатель
$\alpha$-Cuts      174
$\alpha$-defuzzified relation      190
Absolute separation      25
Activation functions      260 303
AD ALINE      294
Artificial neural network      255
Backpropagation      298
Bayes classifier      5 101
Bayes decision rule      108
Bayes formula      100
Bayes risk      115
c-fuzzy partition      194 203
c-Means      77
Cauchy-Schartz inequality      292
Cluster center      56
Clustering      3
Clustering transformation      150
Complement of      171
Conditional risk      108
Covariance matrix      119
Data mining (DM)      9
Decision boundaries      63 278
Decision function      4 22 59
Decision region      29
Defuzzification      199
Defuzzifying      201
Dichotomization capacity      44
Dichotomy      40
Entropy minimization      153
Error probability      124
Feature extraction      2
Feature function      157
Feature selection      2
Feature vector      106
Feature weighting      147
features      1
Finite state grammar      238
Formal language      234
Functional approximation      135 157
Fuzzy c-means      6
Fuzzy c-Means iterative clustering      196
Fuzzy classification      183
Fuzzy cluster      3 196
Fuzzy clustering process      195
Fuzzy event      168
Fuzzy IF-THEN rules      10
Fuzzy relation      178
Fuzzy sets      167
Fuzzy similarity      202
Generalized decision function      32
Grade of membership      168
Grammar G      235
Hebb learning rule      275
Hebb rule      293
Hermite polynomials      53 140
intersection of      172
Interset distance      142 147
Intraset distance      142 144
ISODATA      5 81 96
Knowledge discovery (KD)      9
Lagrange multipliers      130 148
Laguerre polynomials      52
Language      231
Learning of      281
Learning rate      308
Legendre polynomials      52
Level fuzzy sets      175
Likelihood function      112
Likelihood ratio      112
Linear classifier      4
Linear decision boundary      37
Linear dichotomy      40
Linearly implementable      43
Linearly separable      23
Linguistic variable      168
loss      107
Loss matrix      108
Mahalanobis distance      126
Max-min distance method      73
Maximum approaching degree      217
Maximum entropy principle      130
Maximum membership principle      211
McCulloch — Pitts neuron      263
Membership function      168
Minimum entropy transformation      165
Minimum-distance classifier      58 123
Multilayer net      258
Multivariate normal distribution function      118
Natural group      9
Nearest cluster classifier      218
Nearest neighbor      5 65
Nearest neighbor classification      66
Nearest neighbor classifier      218
Neural net      6
Noise samples      96
Normal distribution function      118
Orthonormal system      47
Pairwise separable      27
Parsing      243 248 251
Pattern      1
Pattern primitives      228
Pattern recognition system      2
Perceptron      282
Performance index      69
Positive definite matrix      119
Recognition      1
Reflexivity      184
Robbins — Mouro algorithm      163
Saturation state      137
Sentence      233
Separating zone      287
Similarity      3 69
Single-layer net      257
Stochartic approximation      157
Stochastic grammar      252
Stochastic language      253
Substring      232
Supervised learning      3
Symmetry      184
Syntactic pattern recognition      6 228
The Extension Principle      176
Total risk      108
Training algorithms      98
Transitivity      184
Typical value      57
Uncertainty      153 167
union      171
Univariate Functions      45
Unsupervised learning      3
Unsupervised learning problem      98
Unsupervised pattern recognition      98
Weight matrix      147
Zadeh, Lotfi A.      168
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