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Highlights from
discrim
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confmat(c, d)
CONFMAT Confusion matrix.
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crossval(rule, X, k, v)
CROSSVAL K-fold cross validation.
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mahalanobis(X, Mu, C)
MAHALANOBIS Mahalanobis distance.
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mcnemar(k, a, b)
MCNEMAR McNemar's test for comparison of error rates.
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parseopt(opt, varargin)
PARSEOPT Get values from fields of option struct.
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plotdr(f, varargin)
PLOTDR Plot decision regions for classifier object.
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plotobs(X, k)
PLOTOBS Scatter plot of observation classes
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classifier(X, k, prior)
CLASSIFIER Generic discriminant analysis object.
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lda(X, k, prior, est, nu)
LDA Linear Discriminant Analysis.
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logda(X, k, prior, maxit, est)
LOGDA Logistic Discriminant Analysis.
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qda(X, k, prior, est, nu)
QDA Quadratic Descriminant Analysis.
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softmax(X, k, prior, varargin)
SOFTMAX Multinomial feed-forward neural-network
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Contents.m
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View all files
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| File Information |
| Description |
This is version 0.3 of the Discriminant Analysis Toolbox with major bug fixes and additional functionality. A new class hierarchy is introduced with a single parent class for all classification objects. In addition to linear, quadratic and logistic discriminant analysis, limited feed-forward neural network classification is also made possible using the SOFTMAX multinomial deviance critereon for error minimisation. Additional functions include k-fold cross-validation, confustion matrices and several plotting functions. Many of these algorithms are described in B. D. Ripley (1996) Pattern Recognition and Neural Networks. |
| MATLAB release |
MATLAB 5.2 (R10)
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