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Statistical and Neural Classifiers: An Integrated Approach to Design (Advances in Computer Vision and Pattern Recognition)
 
 
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Statistical and Neural Classifiers: An Integrated Approach to Design (Advances in Computer Vision and Pattern Recognition) [Hardcover]

Sarunas Raudys
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Inside This Book (Learn More)
First Sentence
The main objective of this chapter is to define the terminology to be used and the primary issues to be considered in depth in the chapters that follow. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Concordance (Learn More)
These are the most frequently used words in this book.
algorithms  analysis  approach  asymptotic  bayes  between  boundary  case  classes  classification  classifier  complexity  conditional  cost  covariance  curve  data  decision  density  depends  design  df  different  dimensionality  discriminant  distance  distribution  edc  empirical  equation  error  estimate  example  expected  features  figure  first  fisher  function  gaussian  generalisation  hidden  however  increase  iterations  large  layer  learning  let  linear  matrix  mean  method  minimum  mlp  model  multivariate  need  network  neural  noise  number  obtain  obtained  optimal  order  output  parameters  pattern  perceptron  performance  prior  probability  problem  random  results  rule  sample  section  see  selection  set  size  slp  small  space  standard  statistical  term  therefore  thus  training  two  use  used  values  variables  variance  vectors  weights 
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