Pattern ClassificationUnter Musterklassifikation versteht man die Zuordnung eines physikalischen Objektes zu einer von mehreren vordefinierten Kategorien. Auf dieser Grundlage können Computer Muster erkennen. Das Interesse an diesem Forschungsgebiet hat in den letzten Jahren, besonders im Zuge der Weiterentwicklung neuronaler Netze, stark zugenommen. Die umfassend überarbeitete, erweiterte und jetzt zweifarbig gestaltete Neuauflage beschreibt alle wesentlichen Aspekte der Mustererkennung systematisch und verständlich. Mit Lösungsheft! (01/00) |
Contents
| 1 | |
2 BAYESIAN DECISION THEORY | 20 |
3 MAXIMUMLIKELIHOOD AND BAYESIAN PARAMETER ESTIMATION | 84 |
4 NONPARAMETRIC TECHNIQUES | 161 |
5 LINEAR DISCRIMINANT FUNCTIONS | 215 |
6 MULTILAYER NEURAL NETWORKS | 282 |
7 STOCHASTIC METHODS | 350 |
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 |
Computer Manual in MATLAB to accompany Pattern Classification David G. Stork,Elad Yom-Tov No preview available - 2004 |
Common terms and phrases
annealing approach assume backpropagation Bayes Bayesian benefit bias binary Boltzmann calculate Chapter classifier classify clusters component classifiers configuration Consider convergence corresponding covariance matrix criterion function d-dimensional data set decision boundary decision rule defined definition denote derivation difficult discriminant function distance distribution entropy error rate FIGURE final find finding finite first fish fixed Gaussian given gradient descent grammar Hessian matrix Hidden Markov Models hidden units hyperplane impurity infinite iteration labeled large number linear discriminant linearly separable maximum-likelihood estimate mean methods minimize minimum mixture density nearest-neighbor neural networks node nonlinear normal number of samples obtain optimal output units parameters pattern recognition Perceptron posterior probabilities prior probabilities procedure random Section sequence Show shown solution specific split statistical stochastic sufficient Suppose Theorem tion training data training error training patterns training samples training set tree two-category unsupervised learning variables variance weight vector zero