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

Minimum Description Length (MDL) Principle      80
Minimum risk rule      14
Misclassiflcation costs      13 14 17 58 177
Missing values      17 66 70 76 120 214 216
ML on ML      211
MLP      85—88
Mntalflt      51
Multi Layer Perception      85—88
Multi Layer Perception, functionality      87
Multi-class trees      58 62
Multidimensional scaling      187 190
Multimodality      112
Multivariate analysis of variance (Manova)      20
Multivariate kurtosis      115 170
Multivariate normality      114
Multivariate skewness      115
Mutual information      117 119
Naive Bayes      12 40 216 263
Nearest neighbour      7 35
Nearest neighbour, example      36
Neural network approaches      3 16
Neural networks      5 221 227
Neurons      3
NewlD      12 65 66 68 122 160 218
No data rule      13
Node, hidden      87
Node, impure      57
Node, input      87
Node, output      87
Node, purity      61
Node, winning      102
Noise      56 61 73 79 216 219 223
Noise signal ratio      119
Noisy      57
Noisy data      61
Nonlinear regression      89
Nonparametric density estimator      35
nonparametric methods      16 29
Nonparametric statistics      5
Normal distribution      20
NS.ratio      119 174
Object recognition datasets      180
Observation language      53 229
Odds      25
Optimisation      94
Ordered categories      25
Over-fitting      107
Overfitting      63 64
Parametric methods      16
Partitioning as classification      8
Parzen window      30
Pattern recognition      16
Perception      86 109 232
Performance measures      4
Performance prediction      210
Plug-in estimates      21
Polak — Ribiere      92
Pole balancing      248
Polytrees      43
Polytrees (CASTLE)      12
Polytrees as classifiers      43
Pooled covariance matrix      19
Prediction as classification      8
Preprocessing      120 123
Primary attribute      123
Prior probabilities      13 133
Prior-uniform      100
Probabilistic inference      42
Products of attributes      25
Projection pursuit      37 216
Projection pursuit (SMART)      12
Projection pursuit, classification      38
Propositional learning systems      237
prototypes      230
Pruning      61 63 67—69 96 107 109 194
Pruning, backward      61 64
Pruning, cost complexity      69
Pruning, forward      61
purity      61 62
Purity measure      59
Purity, measure      61
Quadisc      22 121 170 173 193 263
Quadiscr      225 226
Quadratic discriminant      12 17 22 27 214
Quadratic discriminants      193
Quadratic functions of attributes      22
Radial basis function      85
Radial basis function network      93
RAMnets      103
RBF      12 85 93 223 263
Recurrent networks      88

Recursive partitioning      9 12 16
Reduced nearest neighbour      35
Reference class      26
Regression tree      260
Regularisation      23
Relational learning      241
RETIS      260
RG      56
Risk assessment      132
Rule-based methods      10 220
Rule-learning      50
Satellite image dataset      121 143 173
Scaling parameter      32
Scatterplot smoother      39
SDratio      113 170
Secific-to-general      54
Secondary attribute      123
Segmentation dataset      145 218
Selector      56
Shuttle      107
Shuttle dataset      154 218
Simulated digits data      45
skewness      28 115 170
Skew_abs      115
SMART      39 216 224 225 263
Smoothing parameter      32
Smoothing parameters      214
SNR      119
Specific-to-general      54 57 58 79
Speed      7
Splitiing criteria      61
Splitting criteria      61
Splitting criterion      62 67 70 76
SPlus      26
Statistical approaches to classification      2
Statistical measures      112 169
StatLog      1 4
StatLog, collection of data      53
StatLog, objectives      4
StatLog, preprocessing      124
Stepwise selection      11
Stochastic gradient      93
Storage      223
Structured induction      83
Subset selection      199
Sum of squares      18
Supervised learning      1 6 8 85
Supervised networks      86
Supervised vector      102
Supervisor      8
Symbolic learning      52
Symbolic ML      52
Taxonomic      58
Taxonomy      54 57 58 79
Technical dataset      120 161 218
Tertiary attribute      123
test environment      214
Test set      8 17 108
Three-Mile Island      7
Tiling algorithm      96
Time      223 224
Time to learn      7
Time to test      7
Train-and-test      108
Training optimisation      94
Training set      8 17 35 108
Transformation      121
Transformation of attributes      25
Transformations of variables      11
Tree-learning      50
Trees-into-rules      79
Tsetse dataset      167 218
Tuning of parameters      109
UK credit dataset      121
Uniform distribution      32
Univariate kurtosis      116
Univariate skewness      116
Universal approximators      88
Universal Computers      88
Unsupervised learning      1 6 85 101
Upstart      96
User’s guide to algorithms      214
Vector Quantizers      101
Vehicle      170
Vehicle dataset      138
Vertebrate      53 54 57
Vertebrate species      57
Voronoi tessellation      101
XpertRule      65 80 82
Yardstick methods      210
Zero variance      22

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