[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.