Pattern Classification, Part 1This unique text/professional reference provides the information you need to choose the most appropriate method for a given class of problems, presenting an in-depth, systematic account of the major topics in pattern recognition today. A new edition of a classic work that helped define the field for over a quarter century, this practical book updates and expands the original work, focusing on pattern classification and the immense progress it has experienced in recent years."--BOOK JACKET. |
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Page 70
... dimensions ( and likewise for all even dimensions ) . Use your integration results to deter- mine the formula for Va for d odd . ( d ) Use your intermediate integration results to determine Va for d even . ( e ) Explain why we should ...
... dimensions ( and likewise for all even dimensions ) . Use your integration results to deter- mine the formula for Va for d odd . ( d ) Use your intermediate integration results to determine Va for d even . ( e ) Explain why we should ...
Page 74
... dimensional space and then classify them . ( b ) Despite this fact , suggest why in an actual pattern recognition application we might not want to include an arbitrarily high number of feature dimensions . Section 2.8 34. Show that if ...
... dimensional space and then classify them . ( b ) Despite this fact , suggest why in an actual pattern recognition application we might not want to include an arbitrarily high number of feature dimensions . Section 2.8 34. Show that if ...
Page 203
... dimension is O ( d3nd / 2 ) Inn ) . 11. To understand the " curse of dimensionality " in greater depth , consider the ef- fects of high dimensions on the simple nearest - neighbor algorithm . Suppose we need to estimate a density ...
... dimension is O ( d3nd / 2 ) Inn ) . 11. To understand the " curse of dimensionality " in greater depth , consider the ef- fects of high dimensions on the simple nearest - neighbor algorithm . Suppose we need to estimate a density ...
Contents
MAXIMUMLIKELIHOOD AND BAYESIAN | 84 |
NONPARAMETRIC TECHNIQUES | 161 |
LINEAR DISCRIMINANT FUNCTIONS | 215 |
Copyright | |
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Other editions - View all
Computer Manual in MATLAB to accompany Pattern Classification David G. Stork,Elad Yom-Tov No preview available - 2004 |
Computer Manual in MATLAB to accompany Pattern Classification David G. Stork,Elad Yom-Tov No preview available - 2004 |
Common terms and phrases
analysis approach assume backpropagation Bayes Bayesian bias binary Boltzmann calculate Chapter cluster centers component classifiers Consider convergence corresponding covariance matrix criterion function d-dimensional data set decision boundary denote derivation discriminant function distance distribution entropy error rate feature space FIGURE Gaussian given gradient descent Hidden Markov Models hidden units independent input iteration jackknife estimate labeled large number learning algorithm maximum-likelihood estimate mean methods minimize minimum minimum description length mixture density nearest-neighbor neural networks node nonlinear normal number of clusters number of samples obtain optimal output units p(xw parameters pattern recognition Perceptron points prior probabilities probability density problem procedure random variables randomly Section sequence shown shows simple solution split statistical statistically independent string Suppose target tion training data training error training patterns training set tree two-category unsupervised learning variance w₁ weight vector x₁ zero