文件名称:svmTrain
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[model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an
SVM classifier and returns trained model. X is the matrix of training
examples. Each row is a training example, and the jth column holds the
jth feature. Y is a column matrix containing 1 for positive examples
and 0 for negative examples. C is the standard SVM regularization
parameter. tol is a tolerance value used for determining equality of
floating point numbers. max_passes controls the number of iterations
over the dataset (without changes to alpha) before the algorithm quits.- [model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an
SVM classifier and returns trained model. X is the matrix of training
examples. Each row is a training example, and the jth column holds the
jth feature. Y is a column matrix containing 1 for positive examples
and 0 for negative examples. C is the standard SVM regularization
parameter. tol is a tolerance value used for determining equality of
floating point numbers. max_passes controls the number of iterations
over the dataset (without changes to alpha) before the algorithm quits.
SVM classifier and returns trained model. X is the matrix of training
examples. Each row is a training example, and the jth column holds the
jth feature. Y is a column matrix containing 1 for positive examples
and 0 for negative examples. C is the standard SVM regularization
parameter. tol is a tolerance value used for determining equality of
floating point numbers. max_passes controls the number of iterations
over the dataset (without changes to alpha) before the algorithm quits.- [model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an
SVM classifier and returns trained model. X is the matrix of training
examples. Each row is a training example, and the jth column holds the
jth feature. Y is a column matrix containing 1 for positive examples
and 0 for negative examples. C is the standard SVM regularization
parameter. tol is a tolerance value used for determining equality of
floating point numbers. max_passes controls the number of iterations
over the dataset (without changes to alpha) before the algorithm quits.
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svmTrain.m