Michie D., Spiegelhalter D.J., Taylor C.C. — Machine learning, neural and statistical classification :: Электронная библиотека попечительского совета мехмата МГУ

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Michie D., Spiegelhalter D.J., Taylor C.C. — Machine learning, neural and statistical classification

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Название: Machine learning, neural and statistical classification

Авторы: Michie D., Spiegelhalter D.J., Taylor C.C.

Аннотация:

The aim of this book is to provide an up-to-date review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems. Before describing the contents, we first need to define what we mean by classification, give some background to the different perspectives on the task, and introduce the European Community StatLog project whose results form the basis for this book.

Язык:

Рубрика: Computer science/AI, knowledge/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 1994

Количество страниц: 290

Добавлена в каталог: 19.11.2005

Операции: Положить на полку | Скопировать ссылку для форума | Скопировать ID

Предметный указатель

      12 67 123 218 263
$\bar{H}(X)$       174
Accuracy      7 8
ACE      46
Algorithms, function approximation      230
Algorithms, instance-based      230
Algorithms, symbolic learning      230
ALLOC80      33 214 227 263
Alternating Conditional Expectation      46
Analysis of results      176
AOCDL      56
AQ      56 57 74 77
AQ11      50 54
Architectures      86
Assistant      65
Attribute coding      124
Attribute entropy      174
Attribute noise      174
Attribute reduction      120
attribute types      214
Attribute vector      17
Attributes      1
Australian credit dataset      134
B ayes-tree      41
Background knowledge      11 241
Backprop      12 110
Bayes minimum cost rule      13
Bayes rule      40
Bayes theorem      15
Bayes Tree      263
Bayesian evidence      100
Bayesian methods      29
Bayesian networks      41
Bayesian regularisation, Cascade correlation      98
Behavioural cloning      261
Belgian Power 1 dataset      121 163
Belgian Power 11 dataset      164
Bias      120 122
BIFROST      43
Binary attributes      25
binomial      26
Bootstrap      107—109
Boxes      234 250
C4.5      12 63—65 79 80 209 219 264
CAL5      71 72 263
Canonical correlation      114 173
Canonical discriminants      114
Canonical variates      20 114
CART      12 63 64 68 123 126 132 218 219 225 264
Cascade      110
Cascade correlation      12 97 98 263
Castle      45 217 226 263
categorical variables      17
Causal network      41
Causal networks      41
CHAID      62
Chernobyl      7
Chi-square test of independence      119
Choice of variables      11
Chromosome dataset      142
class      8
Class definitions      7
Class entropy      173
Class probability tree      73
Class probability trees      61
Classes      1
Classical discrimination techniques      17
Classification      1 8
Classification rule      6
Classification: definition      6
CLS      52
Clustering      1 6
CN2      12 56 57 73—75 77 79 218 220
Code vector      101
Coding of categories      122
Combination of attributes      121
Combinations of variables      11
Comparative trials      110
COMPLEX      56
Comprehensibility      7 214
Concept      228
Concept learning      51
Concept Learning System      52
Concept-recognisers      62
Condensed nearest neighbour      35
Conditional dependency      53
Conjugate gradient      91
Constructive algorithms      88
Constructive algorithms, pruning      96
Container cranes      258
Controller design      246
Correlation      113
Correspondence analysis      185
Corr_abs      173
Cost datasets      176 183
Cost matrices      214 224
Cost matrix      221
costs      225
Covariance      113
Covariance matrix      19 21
Cover      56
Covering algorithm      237
Credit datasets      7 8 122 132—135 176
Credit management dataset      122 132
Credit scoring      132
Cross validation      107—109
Cross-entropy      89
Cut20 dataset      121 146 181
Cut50 dataset      121 146 181
DAG (directed acyclic graph)      41
Data soybean      50—52
Dataset, Australian credit      134
Dataset, Belgian Power I      163
Dataset, Belgian Power II      164 174
Dataset, characterisation      112
Dataset, chromosomes      142
Dataset, collection      124
Dataset, credit management      132
Dataset, cut      146 181
Dataset, diabetes      157
Dataset, DNA      158
Dataset, German credit dataset      153
Dataset, hand-written digits      135
Dataset, head injury      149
Dataset, heart disease      152
Dataset, image segmentation      145
Dataset, Karhunen — Loeve Digits      137 193
Dataset, letter recognition      140
Dataset, machine faults      165
Dataset, satellite image      143
Dataset, Shuttle      173
Dataset, shuttle control      154 193
Dataset, Technical      174
Dataset, tsetse fly distribution      167
Dataset, vehicle recognition      138
Decision class      14
Decision problems      1
Decision trees      5 9 56 73 109 121 217 226
default      57 80
Default rule      13
Density estimates      12
Density estimation      30
Diabetes dataset      157
Digits dataset      135 181 223
DIPOL92      12 103 223 225 263
Directed Acyclic Graph (DAG)      41
discrim      17 121 126 173 214 225
Discrimination      6 8
Distance      161
Distribution-free methods      16

DNA dataset      23 122 124 158 161 222 226
Domain knowledge      255
dominators      186
EA      111
ECG      52 227
Edited nearest neighbour      35
EN.attr      118
entropy      70 76—78
Entropy estimation      117
Entropy of attributes      116
Entropy of classes      117
Epistemologically adequate      80
Equivalent number of attributes      118
Error rate      194
Error rate estimation      107
Evaluation Assistant      110 111
Examples of classifiers      8
Expert Systems      50
Extensions to linear discrimination      12
features      1
Feed-forward networks      96
Feedforward network      88
First order logic      230
Fisher’s linear discriminant      9 17
Fractk      114
Fract_k      170 173
Gaussian distribution      20
General-to-specific      54 56 57
Generalised Delta Rule      86
Generalised linear models (GLM)      26
Genetic      65
Genetic algorithms      2 5 234 252
German credit      153
Gini function      68
Gini index      68
GLIM      26
GOLEM      81
Gradient descent      90
Gradient descent, MLP      92
Gradient descent, second-order      91
Gradient methods      93
H(C)      117 173
H(C,X)      111
H(X)      116
Head injury dataset      23
Headdataset      149 173
Heart dataset      152 173
Heuristically adequate      80
Hidden nodes      109
Hierarchical clustering      189 192
Hierarchical structure      2
Hierarchy      120 123
Human brain      3
Hypothesis language      54 229
ID3      160 218 219
ILP      65 81 82
Image datasets      176 179 182
Image segmentation      112 181
Impure      60
Impure node      57
Impurity      57 58 60
IND Package      40
IndCART      12 219 263
Indicator variables      26
Inductive learning      254
Inductive logic programming      81 82
Inductive logic programming (ILP)      50
Information measures      116 169
Information score      203
Information theory      116
Iris data      9
Irrelevant attributes      119 226
ISoft dataset      123
ITrule      12 56 57 77 78 220 265
J-measure      56 78
Jackknife      32
Joint entropy      117
K nearest neighbour      160
K-means clustering      102
K-Nearest Neighbour      35
K-NN      160 182 216 224 227 265
K-NN, Cross validation      36
K-R-K problem      80-82
Kalman filter      96
KARDIO      52 227
Kernel density (ALLOC80)      12
Kernel density estimation      30 214
Kernel function      31
Kernel, classifier      33
Kernel, window width      33
kernels      32
KL digits dataset      27 121 137 170
Kohonen      160 222 265
Kohonen networks      85 102
Kohonen self-organising net      12
Kullback-Leibler information      112
kurtosis      22 115 170
Layer, hidden      87
Layer, input      86
Layer, output      86
Learning curves      127
Learning graphical representations      43
Learning Vector Quantization (LVQ)      12
Learning vector quantizer      102
Learning vector quantizers      102
Leave-one-out      108
Letters dataset      140 208
Likelihood ratio      27
Lineal-discriminant      11 12 17 104 214
Lineal-discrimination      27
Lineal-independent      121
Lineal-regression      26 104
Lineal-threshold unit (LTU)      233
Lineal-transformation      115
Lineal-trees      56
Linear decision tree      156
Linesearches      91
Link function      26
Lnstatnce-based learning (IBL)      230
Log likelihood      32
Logdisc      24 121 263
Logistic discriminant      17 24
Logistic discrimination      27
Logistic discrimination-programming      25
LVQ      102 126 221 222 264
M statistic      113
M(C,X)      117
Machine faults dataset      165
Machine learning approaches      16
Machine learning approaches to classification      2
MADALINE      223
Manova      20 114 173
Many categories      121
Marginalisation      98
MARS      47
Maximum conditional likelihood      25
Maximum likelihood      20 25 32
McCulloch — Pitts neuron      84
MDL      80
Measure of collinearity      114
Measures      112 209
Measures of normality      114
Measures, Information-based      169
Measures, statistical      169
Medical datasets      217
Memory      223
Mental fit      50—52 56 79 80
Metalevel learning      197
Minimisation methods      90
Minimum cost rule      14

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